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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
zU2v47WF0Ku | 2,022 | rejected | Implicit Bias of Linear Equivariant Networks | [
"Hannah Lawrence",
"Kristian Georgiev",
"Andrew Dienes",
"Bobak Kiani"
] | [
"~Hannah_Lawrence1",
"~Kristian_Georgiev1",
"adienes@mit.edu",
"~Bobak_Kiani1"
] | OpenReview API | Group equivariant convolutional neural networks (G-CNNs) are generalizations of convolutional neural networks (CNNs) which excel in a wide range of scientific and technical applications by explicitly encoding particular group symmetries, such as rotations and permutations, in their architectures. Although the success o... | Reject | null | 4 | [
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cbtV7xGO9pS | 2,021 | rejected | TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control | [
"Hongyu Zang",
"Xin Li",
"Li Zhang",
"Peiyao Zhao",
"Mingzhong Wang"
] | [
"~Hongyu_Zang1",
"~Xin_Li31",
"~Li_Zhang18",
"~Peiyao_Zhao1",
"~Mingzhong_Wang1"
] | OpenReview API | Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively. This paper proposes to integrate both branches in a unified framework, thus benefiting from both sides. We first ... | Reject | null | 4 | [
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"recommendation": "",
"so... | https://openreview.net/forum?id=cbtV7xGO9pS | null | papers/cbtV7xGO9pS.pdf | 7b08de7608f3ab5794d9a32f5bf2aab60c35465d5b0a2c719d78bed6f1f5fe48 | 3,088,734 | openreview | https://github.com/ICLR2021papersub/TEAC | ICLR2021papersub/TEAC | bad3488749963daf8cc71bc5dcb870e71b97abdd | repos/cbtV7xGO9pS.zip | dd32d7a66f767c10d0e1b463eda84c0ad1fab4931403ef59ceb66b4468fe5650 | 724,386 | 27 | {
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} | 1,368 | {
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} | false | 2020-10-30T06:11:35 | {
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wyCnT4BUsT | 2,026 | rejected | DeepCritic: Deliberate Critique with Large Language Models | [
"Wenkai Yang",
"Jingwen Chen",
"Yankai Lin",
"Ji-Rong Wen"
] | [
"~Wenkai_Yang1",
"~Jingwen_Chen4",
"~Yankai_Lin1",
"~Ji-Rong_Wen1"
] | OpenReview API | As Large Language Models (LLMs) are rapidly evolving, providing accurate feedback and scalable oversight on their outputs becomes an urgent and critical problem. Leveraging LLMs as critique models to achieve automated supervision is a promising solution. In this work, we focus on studying and enhancing the math critiqu... | Reject | 3 | [
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"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces DeepCritic, a two-stage framework for enhancing the... | https://openreview.net/forum?id=wyCnT4BUsT | 2505.00662 | papers/wyCnT4BUsT.pdf | a5bc0f8fdd2ebe2b03684b968e67f72315e68ebc056c2403d248c3647d2372af | 1,271,889 | openreview | https://github.com/RUCBM/DeepCritic | RUCBM/DeepCritic | 53eaf5e048187162451ff165e823c27d976d0e09 | repos/wyCnT4BUsT.zip | d2db3f057f7b21ec1c4c7d9ce0c1e37a2d9af3590a4b845441a60dd01ce51a20 | 4,651,850 | 24 | {
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} | 4,535 | {
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} | false | 2025-06-24T12:50:24 | {
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DDxLsxiZR8 | 2,025 | rejected | CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion Models | [
"Xinle Cheng",
"Zhuoming Chen",
"Zhihao Jia"
] | [
"~Xinle_Cheng1",
"~Zhuoming_Chen1",
"~Zhihao_Jia2"
] | OpenReview API | Diffusion models have transformed generative tasks, particularly in text-to-image synthesis, but their iterative denoising process is computationally intensive. We present a novel acceleration strategy that combines token-level pruning with cache mechanisms to address this challenge. By utilizing Noise Relative Magnitu... | Reject | 4 | [
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"presentation": 2,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces CAT Pruning (Cluster-Aware Token Pruning), an accele... | https://openreview.net/forum?id=DDxLsxiZR8 | 2502.00433 | papers/DDxLsxiZR8.pdf | b0e71fb65d37783c4203afae0f90ff1b3cff73ec508469de303a702aeecb89d7 | 3,146,516 | openreview | https://github.com/ada-cheng/CAT-Pruning | ada-cheng/CAT-Pruning | 3b88889e4deea8f1033b509efe3fd5bab90a7b43 | repos/DDxLsxiZR8.zip | 09f45b3d3140b619917c40786910176793d7e5d301296f6d975da1f82b5fd511 | 334,769 | 18 | {
".py": 18
} | 324 | {
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} | false | 2025-07-26T06:11:14 | {
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tAmfM1sORP | 2,024 | rejected | Large Language Models can Learn Rules | [
"Zhaocheng Zhu",
"Yuan Xue",
"Xinyun Chen",
"Denny Zhou",
"Jian Tang",
"Dale Schuurmans",
"Hanjun Dai"
] | [
"~Zhaocheng_Zhu1",
"~Yuan_Xue5",
"~Xinyun_Chen1",
"~Denny_Zhou1",
"~Jian_Tang1",
"~Dale_Schuurmans1",
"~Hanjun_Dai1"
] | OpenReview API | When prompted with a few examples and intermediate steps, large language models (LLMs) have demonstrated impressive performance in various reasoning tasks. However, prompting methods that rely on implicit knowledge in an LLM often hallucinate incorrect answers when the implicit knowledge is wrong or inconsistent with t... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "3 good",
"presentation": "1 poor",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=tAmfM1sORP | 2310.07064 | papers/tAmfM1sORP.pdf | cbcab388152cd96c31c8f6ea2b79f1966bef41583f08ee6ff5b33cdb222345b9 | 423,040 | openreview | https://github.com/google-deepmind/llms_can_learn_rules | google-deepmind/llms_can_learn_rules | 5c2c523690720a314932dcd8f77c321a3bd9c1f8 | repos/tAmfM1sORP.zip | 3eeb7d588e9298e9c126088986ea527b9ad2ba7c383a91a8a6ff16c204bbc08c | 265,211 | 7 | {
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} | 262 | {
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} | false | 2024-12-06T01:54:21 | {
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7PURWDjJCf3 | 2,023 | rejected | Slimmable Networks for Contrastive Self-supervised Learning | [
"Shuai Zhao",
"Xiaohan Wang",
"Linchao Zhu",
"Yi Yang"
] | [
"~Shuai_Zhao1",
"~Xiaohan_Wang2",
"~Linchao_Zhu1",
"~Yi_Yang22"
] | OpenReview API | Self-supervised learning makes great progress in large model pre-training but suffers in training small models. Previous solutions to this problem mainly rely on knowledge distillation and indeed have a two-stage learning procedure: first train a large teacher model, then distill it to improve the generalization abilit... | Reject | null | 4 | [
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"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces... | https://openreview.net/forum?id=7PURWDjJCf3 | 2209.15525 | papers/7PURWDjJCf3.pdf | 768457d3d4c79d1c6cb0934192c3d524f8ad73eea3bf0d7fb95e7291351d8085 | 2,204,918 | openreview | https://github.com/mzhaoshuai/SlimCLR | mzhaoshuai/SlimCLR | d975c2ab3fb1aaa0c3dbe2520a09ed449970d5cd | repos/7PURWDjJCf3.zip | 966816b32815780d66e617f915c1af091753185541fb30c529b979f78f2a4696 | 629,787 | 155 | {
".py": 146,
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} | 582 | {
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} | false | 2025-11-18T04:07:40 | {
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Qu_XudmGajz | 2,022 | rejected | Structured Uncertainty in the Observation Space of Variational Autoencoders | [
"James Langley",
"Miguel Monteiro",
"Charles Jones",
"Nick Pawlowski",
"Ben Glocker"
] | [
"~James_Langley1",
"~Miguel_Monteiro1",
"~Charles_Jones4",
"~Nick_Pawlowski2",
"~Ben_Glocker1"
] | OpenReview API | Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus on the modelling of the posterior distribution over the latent space and the properties of the neural network decoder. In contrast, improving... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=Qu_XudmGajz | 2205.12533 | papers/Qu_XudmGajz.pdf | baad970b9fe313794529d8fc7729e3b6b34752b6af86a988c1caa959bf8a5af1 | 47,036,636 | openreview | https://github.com/biomedia-mira/sos-vae | biomedia-mira/sos-vae | cc02afb2551dee5b472741dce3d1e82e0996c83a | repos/Qu_XudmGajz.zip | 12e59ab95dd7d30e72f07eb9a745e0b6c973405eb6c5d6fe0b3749a7207ce0c7 | 2,262,927 | 27 | {
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} | 2,204 | {
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} | false | 2022-10-28T22:11:51 | {
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lJgbDxGhJ4r | 2,021 | rejected | OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data | [
"Jongjin Park",
"Sukmin Yun",
"Jongheon Jeong",
"Jinwoo Shin"
] | [
"~Jongjin_Park1",
"~Sukmin_Yun1",
"~Jongheon_Jeong1",
"~Jinwoo_Shin1"
] | OpenReview API | Modern semi-supervised learning methods conventionally assume both labeled and unlabeled data have the same class distribution. However, unlabeled data may include out-of-class samples in practice; those that cannot have one-hot encoded labels from a closed-set of classes in label data, i.e., unlabeled data is an open-... | Reject | null | 4 | [
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"... | https://openreview.net/forum?id=lJgbDxGhJ4r | 2107.08943 | papers/lJgbDxGhJ4r.pdf | a4fc42ef14d533800188e561a531b632211d36ab862dd782ef0b7f02cf2148c6 | 1,422,343 | openreview | https://github.com/alinlab/OpenCoS | alinlab/OpenCoS | 59003724045f82cf1ca54b2d509da7abc5aefe96 | repos/lJgbDxGhJ4r.zip | 6b8950d86121ad9c904ac288f5d6972dc2312275058e69a3bfc8c63a58a6a078 | 1,502,669 | 31 | {
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} | 1,425 | {
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} | false | 2022-06-16T08:04:53 | {
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6rm4ZC1nnP | 2,026 | rejected | LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control | [
"Peiyao Xiao",
"Chaosheng Dong",
"Shaofeng Zou",
"Kaiyi Ji"
] | [
"~Peiyao_Xiao1",
"~Chaosheng_Dong1",
"~Shaofeng_Zou1",
"~Kaiyi_Ji1"
] | OpenReview API | Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced solutions than simple scalarization-based approaches, they typically incur a significant computational overhead of $\mathcal{O}(K)$ in both tim... | Reject | 4 | [
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"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 4,
"summary": "The paper proposes LDC-MTL, a scalable loss discrepancy control method for... | https://openreview.net/forum?id=6rm4ZC1nnP | 2502.08585 | papers/6rm4ZC1nnP.pdf | a1cb15bb99381f7a685fd643cfc7b43ccbb1c3393936e8e0fa4cf9b760c69fba | 1,512,519 | openreview | https://github.com/OptMN-Lab/LDC-MTL | OptMN-Lab/LDC-MTL | 7c3946518689bf05be196c7bd51947b2cf885c9e | repos/6rm4ZC1nnP.zip | b659cca14ea0b2b5787299f6f00a17213201b26367d66fba2a84d7eb352b6b30 | 3,543,370 | 24 | {
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} | 4,871 | {
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} | false | 2025-05-15T16:15:10 | {
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ogmzNfeRl7 | 2,025 | rejected | Correlations Are Ruining Your Gradient Descent | [
"Nasir Ahmad"
] | [
"~Nasir_Ahmad1"
] | OpenReview API | Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a common discussion. Natural gradient descent illuminates how gradient vectors, pointing at directions of steepest descent, can be improved by considering the local curvature of loss landsca... | Reject | 3 | [
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"presentation": 3,
"contribution": 4,
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"summary": "Starting from natural gradient descent, the authors show that correlations... | https://openreview.net/forum?id=ogmzNfeRl7 | 2407.10780 | papers/ogmzNfeRl7.pdf | 613e8814a56b2b3850c33e230833b73d32bae9ce91cf3e7c0e94c2af0e3cc46f | 1,715,785 | openreview | https://github.com/nasiryahm/CorrelationsRuinGD | nasiryahm/CorrelationsRuinGD | 00cf47050bbd20e6a153e10bd86e1651524f9779 | repos/ogmzNfeRl7.zip | 880c79deed5c0065c46f1d09d3963ac9dda1ef9dcae8b87c9eccff103ba647ac | 80,833 | 10 | {
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} | 329 | {
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} | false | 2025-06-25T09:21:55 | {
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Q8cVivO5k5 | 2,024 | rejected | Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization | [
"Navid Ansari",
"Hans-peter Seidel",
"Vahid Babaei"
] | [
"~Navid_Ansari1",
"~Hans-peter_Seidel1",
"~Vahid_Babaei1"
] | OpenReview API | Bayesian optimization (BO) provides a powerful framework for optimizing black-box, expensive-to-evaluate functions. It is therefore an attractive tool for engineering design problems, typically involving multiple objectives. Thanks to the rapid advances in fabrication and measurement methods as well as parallel computi... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "2 fair",
"presentation": "2 fair",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=Q8cVivO5k5 | 2306.01095 | papers/Q8cVivO5k5.pdf | f97e8cccb755588400aeb1d9819a025066426fca479a6f88ac461925b2467efe | 7,788,836 | openreview | https://github.com/AnsariNavid/lbn_mobo | AnsariNavid/lbn_mobo | da980750ecea1ed386d9ff54c16f6ae9c279e905 | repos/Q8cVivO5k5.zip | 04c4f3012720256a5464e793d4d5bcd545baf628f5cc8624e6bf4be97931e9cd | 211,249 | 67 | {
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} | 264 | {
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} | false | 2023-05-22T14:44:37 | {
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NHfSJAWhKTw | 2,023 | rejected | A Closer Look at Self-supervised Lightweight Vision Transformers | [
"Shaoru Wang",
"Jin Gao",
"Zeming Li",
"Weiming Hu"
] | [
"~Shaoru_Wang1",
"~Jin_Gao1",
"~Zeming_Li2",
"~Weiming_Hu1"
] | OpenReview API | Self-supervised learning on large-scale Vision Transformers (ViTs) as pre-training methods has achieved promising downstream performance. Yet, how much these pre-training paradigms promote lightweight ViTs' performance is considerably less studied. In this work, we mainly develop and benchmark self-supervised pre-train... | Reject | null | 3 | [
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ZzwfldvDLpC | 2,022 | rejected | Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals | [
"Dani Kiyasseh",
"Tingting Zhu",
"David A. Clifton"
] | [
"~Dani_Kiyasseh1",
"~Tingting_Zhu1",
"~David_A._Clifton1"
] | OpenReview API | Cardiac signals convey a significant amount of information about the health status of a patient. Upon recording these signals, cardiologists are expected to manually generate an accompanying report to share with physicians and patients. Generating these reports, however, can be time-consuming and error-prone, while als... | Reject | null | 4 | [
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"recommendati... | https://openreview.net/forum?id=ZzwfldvDLpC | 2103.11011 | papers/ZzwfldvDLpC.pdf | eabcf26638e62b32986478cf48f85ea5299213d4c7a74a3bc14133df4bbdcc72 | 4,227,129 | openreview | https://github.com/danikiyasseh/RTLP | danikiyasseh/RTLP | e52f3e2488ba2d5eb6be0fa011daac002e736c16 | repos/ZzwfldvDLpC.zip | 5610078a8f05b39912a7cbbe2438d94353f900eb4f5f74920cf88e149e0f1a78 | 1,567,052 | 15 | {
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} | 2,341 | {
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} | false | 2022-08-25T16:43:17 | {
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j5d9qacxdZa | 2,021 | rejected | Energy-Based Models for Continual Learning | [
"Shuang Li",
"Yilun Du",
"Gido Martijn van de Ven",
"Antonio Torralba",
"Igor Mordatch"
] | [
"~Shuang_Li5",
"~Yilun_Du1",
"~Gido_Martijn_van_de_Ven1",
"~Antonio_Torralba1",
"~Igor_Mordatch4"
] | OpenReview API | We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs have a natural way to support a dynamically-growing number of tasks and classes and less interference with old... | Reject | null | 4 | [
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"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"s... | https://openreview.net/forum?id=j5d9qacxdZa | 2011.12216 | papers/j5d9qacxdZa.pdf | d82cdf180c473dde5a7fcb2607cc9f5ea4dde8d4e4c3ab761c541718ae4fdea6 | 4,948,780 | openreview | https://github.com/ShuangLI59/ebm-continual-learning | ShuangLI59/ebm-continual-learning | 0450d69ac01625c1d227356e5367e002aaae65a4 | repos/j5d9qacxdZa.zip | f12f2ed64e2c5a7d6e808a266336af2c0dcff2959bb426be6e70a50b426e013e | 1,536,377 | 41 | {
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FnwU7ogRzv | 2,026 | rejected | CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution | [
"Minghao Shao",
"Haoran Xi",
"NANDA RANI",
"Meet Udeshi",
"Venkata Sai Charan Putrevu",
"Kimberly Milner",
"Brendan Dolan-Gavitt",
"Sandeep K. Shukla",
"Prashanth Krishnamurthy",
"Farshad Khorrami",
"Ramesh Karri",
"Muhammad Shafique"
] | [
"~Minghao_Shao3",
"~Haoran_Xi1",
"~NANDA_RANI1",
"~Meet_Udeshi1",
"~Venkata_Sai_Charan_Putrevu1",
"~Kimberly_Milner1",
"~Brendan_Dolan-Gavitt1",
"~Sandeep_K._Shukla1",
"~Prashanth_Krishnamurthy1",
"~Farshad_Khorrami1",
"~Ramesh_Karri1",
"~Muhammad_Shafique1"
] | OpenReview API | Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated cybersecurity capabilities on Capture-The-Flag (CTF) competitions, they have two key limitations: accessing latest cybersecurity expertise b... | Reject | 4 | [
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"summary": "This paper introduces a system for solving cybersecurity Capture-The-Flag... | https://openreview.net/forum?id=FnwU7ogRzv | 2505.17107 | papers/FnwU7ogRzv.pdf | b9d83bb69e045abc6e4e42de67e57f736f97e56e54958164c761427d1dbee26d | 874,852 | openreview | https://github.com/NYU-LLM-CTF/nyuctf_agents_craken | NYU-LLM-CTF/nyuctf_agents_craken | 748bc8986bae2e02eb69dbebce07006a8b325a73 | repos/FnwU7ogRzv.zip | e08a3112d197e51e8294a3ae805dad3198236a528c517e95087c00a4f0495604 | 54,704 | 19 | {
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} | 5,291 | {
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2bn7gayfz9 | 2,025 | rejected | CTBench: A Library and Benchmark for Certified Training | [
"Yuhao Mao",
"Stefan Balauca",
"Martin Vechev"
] | [
"~Yuhao_Mao1",
"~Stefan_Balauca1",
"~Martin_Vechev1"
] | OpenReview API | Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often evaluated on different training schedules, certification methods, and systematically under-tuned hyperparameters, making it difficult to compa... | Reject | 4 | [
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"confidence": 4,
"summary": "The paper presents CTBENCH, a standardized library and benchmark designed ... | https://openreview.net/forum?id=2bn7gayfz9 | 2406.04848 | papers/2bn7gayfz9.pdf | f74b6a53ba22f84d39e82c30946768df6f2e691a42e7a2632fd250123dc0b8e7 | 422,592 | openreview | https://github.com/eth-sri/CTBench | eth-sri/CTBench | 0f18162cba85c54d7fc28af62b2c5926efd3695d | repos/2bn7gayfz9.zip | ad1d962e99a18871f250d4d0fb4bb19106535b7c6348e0b8e1a676106214be26 | 214,501 | 93 | {
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} | false | 2026-04-25T17:55:36 | {
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hJEMTDOwKx | 2,024 | rejected | Language Models as Semantic Indexers | [
"Bowen Jin",
"Hansi Zeng",
"Guoyin Wang",
"Xiusi Chen",
"Tianxin Wei",
"Ruirui Li",
"Zhengyang Wang",
"Zheng Li",
"Yang Li",
"Hanqing Lu",
"Suhang Wang",
"Jiawei Han",
"Xianfeng Tang"
] | [
"~Bowen_Jin1",
"~Hansi_Zeng1",
"~Guoyin_Wang1",
"~Xiusi_Chen1",
"~Tianxin_Wei1",
"~Ruirui_Li3",
"~Zhengyang_Wang1",
"~Zheng_Li9",
"~Yang_Li80",
"~Hanqing_Lu3",
"~Suhang_Wang1",
"~Jiawei_Han1",
"~Xianfeng_Tang1"
] | OpenReview API | Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deri... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=hJEMTDOwKx | 2310.07815 | papers/hJEMTDOwKx.pdf | 05ff3e71cb4c675b5bfbeef76d4e1b5b04bff322c831d71e98419cc129199f1a | 1,254,820 | openreview | https://github.com/PeterGriffinJin/LMIndexer | PeterGriffinJin/LMIndexer | e6dc4cc1ddf1f1615f643771c9047063b8e49528 | repos/hJEMTDOwKx.zip | 8ca1c376945ef5828c23a88ea6cc60771394f74e0a08f2932d6bc2426cc4cb63 | 316,165 | 97 | {
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} | 267 | {
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} | false | 2024-05-02T12:48:23 | {
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hUr6K4D9f7P | 2,022 | rejected | Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks | [
"Yihan Wu",
"Aleksandar Bojchevski",
"Heng Huang"
] | [
"~Yihan_Wu1",
"~Aleksandar_Bojchevski1",
"~Heng_Huang1"
] | OpenReview API | There is growing theoretical and empirical evidence that flatter local minima tend to improve generalization. An efficient and effective technique for finding such minima is Adversarial Weight Perturbation (AWP). The main idea is to minimize the loss w.r.t. a bounded worst-case perturbation of the model parameters by (... | Reject | null | 4 | [
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"confidence": "2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar w... | https://openreview.net/forum?id=hUr6K4D9f7P | 2212.04983 | papers/hUr6K4D9f7P.pdf | c541437eda108e345a55204f7db67e35e187f3ef76edbf29fdc940ce104139f3 | 1,108,766 | openreview | https://github.com/yihwu/WT-AWP | yihwu/WT-AWP | aa0dae0c64521d885fed6dce32c5a35a3a292f3e | repos/hUr6K4D9f7P.zip | aa802b68a1dfa8e2defeb045525eb97e555f6f6f3e6769c0a98bfec8c0f01402 | 2,581,455 | 59 | {
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} | 2,349 | {
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} | false | 2022-11-22T07:28:42 | {
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zbEupOtJFF | 2,021 | rejected | On interaction between augmentations and corruptions in natural corruption robustness | [
"Eric Mintun",
"Alexander Kirillov",
"Saining Xie"
] | [
"~Eric_Mintun1",
"~Alexander_Kirillov1",
"~Saining_Xie2"
] | OpenReview API | Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed that significantly improve performance on ImageNet-C, a benchmark of such corruptions. However, ther... | Reject | null | 4 | [
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"... | https://openreview.net/forum?id=zbEupOtJFF | 2102.11273 | papers/zbEupOtJFF.pdf | c9a3fe7b9361c8a05078ba00cc2271f420f6adb682c789ef46803f3865ee4a6e | 6,672,450 | openreview | https://github.com/facebookresearch/augmentation-corruption | facebookresearch/augmentation-corruption | ba4d5a5e9132fe98fcd4be1ac90abedbbb188794 | repos/zbEupOtJFF.zip | f536df67ed4b1ed909f05970784c4d9b43a83f28810bb2e7e8b032a72ca7092d | 1,586,667 | 74 | {
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} | true | 2022-11-06T23:27:04 | {
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Tj5rHP8yrQ | 2,026 | rejected | CompeteSMoE - Statistically Guaranteed Mixture of Experts Training via Competition | [
"Nam V. Nguyen",
"Huy Nguyen",
"Quang Pham",
"Van Nguyen",
"Savitha Ramasamy",
"Nhat Ho"
] | [
"~Nam_V._Nguyen1",
"~Huy_Nguyen5",
"~Quang_Pham1",
"~Van_Nguyen3",
"~Savitha_Ramasamy1",
"~Nhat_Ho1"
] | OpenReview API | Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, we argue that effective SMoE training remains challenging because of the suboptimal routing process where experts that perform computation do not directly c... | Reject | 3 | [
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"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces a novel competition-based training mechanism for SM... | https://openreview.net/forum?id=Tj5rHP8yrQ | 2505.13380 | papers/Tj5rHP8yrQ.pdf | 2129ac6aa9e0482889ed7975d6b3b3c41e47f09f7c3b7b1f387fb05193c493d9 | 832,107 | openreview | https://github.com/Fsoft-AIC/CompeteSMoE | Fsoft-AIC/CompeteSMoE | ab48bb62aa7edb855328e375b9e089e9b922c9d7 | repos/Tj5rHP8yrQ.zip | ff551a9b49cb66865030ec64c3f857b3aed36e3733cf604f13e094b3b6983101 | 6,254,766 | 449 | {
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} | 5,686 | {
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} | false | 2025-08-23T08:02:14 | {
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GYk0thSY1M | 2,025 | rejected | Recurrent Context Compression: Efficiently Expanding the Context Window of LLM | [
"ChensenHuang",
"Guibo Zhu",
"Xuepeng Wang",
"Dong Yi",
"Yifei Luo",
"Haoran Chen",
"Guojing Ge",
"Jinqiao Wang"
] | [
"~ChensenHuang1",
"~Guibo_Zhu1",
"~Xuepeng_Wang1",
"~Dong_Yi2",
"~Yifei_Luo2",
"~Haoran_Chen1",
"~Guojing_Ge2",
"~Jinqiao_Wang1"
] | OpenReview API | To extend the context length of Transformer-based large language models (LLMs) and improve comprehension capabilities, researchers often encounter constraints stemming from finite computational resources and bounded memory capacities. This work proposes a novel approach, termed Recurrent Context Compression (RCC), desi... | Reject | 4 | [
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"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces Recurrent Context Compression (RCC), a technique for ... | https://openreview.net/forum?id=GYk0thSY1M | 2406.06110 | papers/GYk0thSY1M.pdf | b628086ad454173cf7779ed38cd7383a4eae84e8bc94934859d635547f9439d1 | 587,121 | openreview | https://github.com/WUHU-G/RCC_Transformer | WUHU-G/RCC_Transformer | b9c5486c49708cd284dbf1229fe6bc054d424453 | repos/GYk0thSY1M.zip | d95d1c0e10e74490ebfe2dfb319de02fcf96746a3c7a4cc88b37dbbedff2a15e | 274,055 | 3 | {
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} | 339 | {
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U2ZIgcrg7Z | 2,024 | rejected | ZOOPFL: EXPLORING BLACK-BOX FOUNDATION MODELS FOR PERSONALIZED FEDERATED LEARNING | [
"Wang Lu",
"Hao Yu",
"Jindong Wang",
"Damien Teney",
"Haohan Wang",
"Yiqiang Chen",
"Qiang Yang",
"Xing Xie",
"Xiangyang Ji"
] | [
"~Wang_Lu2",
"~Hao_Yu8",
"~Jindong_Wang1",
"~Damien_Teney1",
"~Haohan_Wang1",
"~Yiqiang_Chen1",
"~Qiang_Yang1",
"~Xing_Xie3",
"~Xiangyang_Ji1"
] | OpenReview API | When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data, computation, and communication costs, access to the models is also often limited. This paper endeavors to solve both the challenges of lim... | Reject | 4 | [
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"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=U2ZIgcrg7Z | 2310.05143 | papers/U2ZIgcrg7Z.pdf | a51156d337e77165e8ec1552603dd54bedafe5d76c58c3bb6895b3feb3cb4225 | 2,140,291 | openreview | https://github.com/microsoft/PersonalizedFL | microsoft/PersonalizedFL | 441670edb5744d23af46867cce9a5fbec0f8ead3 | repos/U2ZIgcrg7Z.zip | 521bb8e68c430baf3e428468640c02aba5f11ce74194fbed0c9acd9ae583d203 | 264,542 | 22 | {
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Gb2Rndy5595 | 2,023 | rejected | Context Autoencoder for Self-Supervised Representation Learning | [
"Xiaokang Chen",
"Mingyu Ding",
"Xiaodi Wang",
"Ying Xin",
"Shentong Mo",
"Yunhao Wang",
"Shumin Han",
"Ping Luo",
"Gang Zeng",
"Jingdong Wang"
] | [
"~Xiaokang_Chen1",
"~Mingyu_Ding1",
"~Xiaodi_Wang2",
"~Ying_Xin1",
"~Shentong_Mo1",
"~Yunhao_Wang1",
"~Shumin_Han1",
"~Ping_Luo2",
"~Gang_Zeng1",
"~Jingdong_Wang1"
] | OpenReview API | We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. The goal is to pretrain an encoder by solving the pretext task: estimate the masked patches from the visible patches in an image. Our approach first feeds the visible patches into the enco... | Reject | null | 4 | [
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UFYYol-bRq | 2,022 | rejected | ANCER: Anisotropic Certification via Sample-wise Volume Maximization | [
"Francisco Eiras",
"Motasem Alfarra",
"Philip Torr",
"M. Pawan Kumar",
"Puneet K. Dokania",
"Bernard Ghanem",
"Adel Bibi"
] | [
"~Francisco_Eiras1",
"~Motasem_Alfarra1",
"~Philip_Torr1",
"~M._Pawan_Kumar1",
"~Puneet_K._Dokania1",
"~Bernard_Ghanem1",
"~Adel_Bibi1"
] | OpenReview API | Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale. All prior art on randomized smoothing has focused on isotropic $\ell_p$ certification, which has the advantage of yielding certificates that can be easily compared among isotropic metho... | Reject | null | 4 | [
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"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piece... | https://openreview.net/forum?id=UFYYol-bRq | 2107.04570 | papers/UFYYol-bRq.pdf | be7e9b329ebbd8c439ef1f81e3e4f0137de709fa6817e36a2a855dc472af25d4 | 5,273,045 | openreview | https://github.com/MotasemAlfarra/ANCER | MotasemAlfarra/ANCER | 98f869e924c482ac246c2245ae3b247e08c7e04b | repos/UFYYol-bRq.zip | 5c6a45368fd08960b3750c07b16eccb551b730fda282cf253222b049e909a1fb | 270,942 | 8 | {
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} | 2,392 | {
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} | false | 2022-09-09T08:45:29 | {
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} | {
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Q1aiM7sCi1 | 2,021 | rejected | Fuzzy c-Means Clustering for Persistence Diagrams | [
"Thomas Davies",
"Jack Aspinall",
"Bryan Wilder",
"Long Tran-Thanh"
] | [
"~Thomas_Davies1",
"jack.aspinall@materials.ox.ac.uk",
"~Bryan_Wilder1",
"long.tran-thanh@warwick.ac.uk"
] | OpenReview API | Persistence diagrams concisely represent the topology of a point cloud whilst having strong theoretical guarantees. Most current approaches to integrating topological information into machine learning implicitly map persistence diagrams to a Hilbert space, resulting in deformation of the underlying metric structure whi... | Reject | null | 4 | [
{
"id": "6wTpzgLuFL2",
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],
"rating": "4: Ok but not good enough - rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"sound... | https://openreview.net/forum?id=Q1aiM7sCi1 | 2006.02796 | papers/Q1aiM7sCi1.pdf | c91cae8d7bf52f22210719cd3a4a51a0415206c53211909163e45557211892b3 | 1,655,816 | openreview | https://github.com/tomogwen/fpdcluster | tomogwen/fpdcluster | 8bcd7c01e6120893a545aaaac1e73d793b6210f0 | repos/Q1aiM7sCi1.zip | 28e21f59c6dc8f3325aab16023e01ec5da4744b6049cffdda912cb48f27e2b18 | 1,023,682 | 5 | {
".py": 5
} | 1,534 | {
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} | false | 2023-10-14T12:00:26 | {
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} | {
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mAutPdnHIN | 2,026 | rejected | ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark | [
"Michael Shalyt",
"Rotem Elimelech",
"Ido Kaminer"
] | [
"~Michael_Shalyt1",
"~Rotem_Elimelech1",
"~Ido_Kaminer1"
] | OpenReview API | Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization with genuine reasoning. To address this gap, we present **ASyMOB**, a high-resolution dataset of **35,368** validated symbolic math problems spanning integration, limits, different... | Reject | 3 | [
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"rating": 4,
"soundness": 3,
"presentation": 2,
"contribution": 2,
"confidence": 3,
"summary": "This work introduces ASyMOB, a 35,368 problem benchmark for symbolic math... | https://openreview.net/forum?id=mAutPdnHIN | 2505.23851 | papers/mAutPdnHIN.pdf | 7b02a17294644008602dd70a7d99e4fbc8aeebd30ec0bc30a6f01d455e56915b | 899,298 | openreview | https://github.com/RamanujanMachine/ASyMOB | RamanujanMachine/ASyMOB | 172712d0a12eed39a47cd1df82f690121c57520d | repos/mAutPdnHIN.zip | 166001cc68ff36168a2231af4f327a372b7af0235df1a7b8f571c1093f5e3337 | 1,439,949 | 17 | {
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".sql": 1
} | 5,714 | {
"Python": 96250
} | false | 2026-06-08T23:49:01 | {
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} | {
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} | |
0fwJMANq9P | 2,025 | rejected | Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models | [
"Xuan Wu",
"Di Wang",
"Zhiguang Cao",
"Chunguo Wu",
"Lijie Wen",
"Chunyan Miao",
"Yubin Xiao",
"You Zhou"
] | [
"~Xuan_Wu7",
"~Di_Wang26",
"~Zhiguang_Cao1",
"~Chunguo_Wu1",
"~Lijie_Wen1",
"~Chunyan_Miao1",
"~Yubin_Xiao1",
"~You_Zhou5"
] | OpenReview API | Recent studies exploited Large Language Models (LLMs) to autonomously generate heuristics for solving Combinatorial Optimization Problems (COPs), by prompting LLMs to first provide search directions and then derive heuristics accordingly. However, the absence of task-specific knowledge in prompts often leads LLMs to pr... | Reject | 4 | [
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"rating": 5,
"soundness": 2,
"presentation": 2,
"contribution": 3,
"confidence": 3,
"summary": "The paper presents Hercules, an LLM-based algorithm for generating heuris... | https://openreview.net/forum?id=0fwJMANq9P | 2505.12627 | papers/0fwJMANq9P.pdf | 3e4d0c5fb444d440dde3c0e1e1c341f5225801123bc032eefed33ffe68a628bf | 668,719 | openreview | https://github.com/wuuu110/Hercules | wuuu110/Hercules | bc532df7325373c564e1926916c8be6bbb645216 | repos/0fwJMANq9P.zip | 5bf3ec189239dda797d2257ef7c958deff95c6afd4e9dab7d9e365332db6b612 | 200,195 | 51 | {
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} | 340 | {
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} | false | 2025-05-20T05:59:36 | {
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} | {
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w8eCnnq57m | 2,024 | rejected | LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition | [
"Chengsong Huang",
"Qian Liu",
"Bill Yuchen Lin",
"Chao Du",
"Tianyu Pang",
"Min Lin"
] | [
"~Chengsong_Huang1",
"~Qian_Liu2",
"~Bill_Yuchen_Lin1",
"~Chao_Du1",
"~Tianyu_Pang1",
"~Min_Lin1"
] | OpenReview API | Low-rank adaptations (LoRA) are often employed to fine-tune large language models (LLMs) for new tasks. This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objectiv... | Reject | 3 | [
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"id": "WMWqjIwSXA",
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],
"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in your... | https://openreview.net/forum?id=w8eCnnq57m | 2307.13269 | papers/w8eCnnq57m.pdf | 4ce8c9e6faf96627c129463d6db335743eb6ab6445e202e583d86b32c45a6a26 | 557,225 | openreview | https://github.com/sail-sg/lorahub | sail-sg/lorahub | df73afe5f38d9ff0fd1cd43774be51c79c581cc3 | repos/w8eCnnq57m.zip | c4d5490c774ebc9ea13f7ca51137a75fa12b71836a3d7306b640aea101ba58b3 | 268,071 | 9 | {
".py": 8,
".sh": 1
} | 281 | {
"Python": 110046,
"Shell": 1501
} | false | 2024-07-22T00:38:28 | {
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} | {
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fUX3bszZSOw | 2,023 | rejected | Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection | [
"XiaoHui Zhang",
"Jiangyan Yi",
"Chenglong Wang",
"Chu Yuan Zhang",
"Jianhua Tao"
] | [
"~XiaoHui_Zhang4",
"~Jiangyan_Yi1",
"~Chenglong_Wang5",
"~Chu_Yuan_Zhang1",
"~Jianhua_Tao2"
] | OpenReview API | Current fake audio detection algorithms achieve promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a different dataset. The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of some audio, includi... | Reject | null | 4 | [
{
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"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piece... | https://openreview.net/forum?id=fUX3bszZSOw | 2308.03300 | papers/fUX3bszZSOw.pdf | 4e3e72b1c89b0085d90f2aae4cf92eacd32aee980d6e69703c3961fd01e091b5 | 1,633,379 | openreview | https://github.com/Cecile-hi/Regularized-Adaptive-Weight-Modification | Cecile-hi/Regularized-Adaptive-Weight-Modification | bb663ee083d8d9f8309701f2255b87916eff55c6 | repos/fUX3bszZSOw.zip | 06be15f23719a8053871703285a433ab3dbed68ee8d4c151d00b8a840d1ff4a3 | 714,128 | 156 | {
".py": 154,
".sh": 2
} | 647 | {
"Python": 1307444,
"Shell": 363
} | false | 2024-09-26T08:14:30 | {
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} | {
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Yn4CPz_LRKO | 2,022 | rejected | Conditional GANs with Auxiliary Discriminative Classifier | [
"Liang Hou",
"Qi Cao",
"Huawei Shen",
"Xueqi Cheng"
] | [
"~Liang_Hou1",
"~Qi_Cao1",
"~Huawei_Shen1",
"~Xueqi_Cheng1"
] | OpenReview API | Conditional generative models aim to learn the underlying joint distribution of data and labels, and thus realize conditional generation. Among them, auxiliary classifier generative adversarial networks (AC-GAN) have been widely used, but suffer from the problem of low intra-class diversity on generated samples. In thi... | Reject | null | 4 | [
{
"id": "LbdeIQnr2Df",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper1198/Reviewer_mZT7"
],
"rating": "",
"confidence": "5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.",
"recommendat... | https://openreview.net/forum?id=Yn4CPz_LRKO | 2107.10060 | papers/Yn4CPz_LRKO.pdf | 6082ae521f88fe5a135412119572edf86d9fa40aad32849c5cc27253d2f8db6c | 1,694,234 | openreview | https://github.com/liang-hou/adcgan | liang-hou/adcgan | 29ecfa74dff78286e5966035a2ec8c6e96e6b4e1 | repos/Yn4CPz_LRKO.zip | e55345ca538a7bd1d6e712da66a5db6798ff9092116aaafdc7a96f020a368203 | 2,634,450 | 48 | {
".sh": 25,
".py": 23
} | 2,613 | {
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} | false | 2023-06-11T17:18:28 | {
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VbCVU10R7K | 2,021 | rejected | Offline policy selection under Uncertainty | [
"Mengjiao Yang",
"Bo Dai",
"Ofir Nachum",
"George Tucker",
"Dale Schuurmans"
] | [
"~Mengjiao_Yang1",
"~Bo_Dai1",
"~Ofir_Nachum1",
"~George_Tucker1",
"~Dale_Schuurmans1"
] | OpenReview API | The presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. We formally consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset. While one can select or rank policies bas... | Reject | null | 3 | [
{
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"rating": "6: Marginally above acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"p... | https://openreview.net/forum?id=VbCVU10R7K | 2012.06919 | papers/VbCVU10R7K.pdf | 9e5ff9eba10b12dc490ce3399498f90b99c00ec05c8a07b5088c432ba98a1cfd | 5,387,397 | openreview | https://github.com/google-research/dice_rl | google-research/dice_rl | 5ee67f7f2145d295bdc4759b62e5c6193af31acf | repos/VbCVU10R7K.zip | cc05b5fa6d97faaf799830bbfde82cd9de5afdaa9d595e8ff33781f76b60acb5 | 1,415,580 | 98 | {
".py": 96,
".sh": 2
} | 1,569 | {
"Python": 582029,
"Shell": 1366
} | false | 2026-07-30T00:11:12 | {
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KuhCUX2oIt | 2,026 | rejected | LEAD: Large Foundation Model for EEG-Based Alzheimer’s Disease Detection | [
"Yihe Wang",
"Nan Huang",
"Nadia Mammone",
"Marco Cecchi",
"Xiang Zhang"
] | [
"~Yihe_Wang2",
"~Nan_Huang2",
"~Nadia_Mammone1",
"~Marco_Cecchi1",
"~Xiang_Zhang10"
] | OpenReview API | Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer’s disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face two major challenges: 1) the lack of large-scale EEG-AD datasets for ro... | Reject | 4 | [
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"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The paper introduces LEAD, a large-scale model for EEG-based Alzheimer’s D... | https://openreview.net/forum?id=KuhCUX2oIt | 2502.01678 | papers/KuhCUX2oIt.pdf | 28d845a6a3c2bf6aaad13d787b5a3f87b6d8778abed3b42124a598a427286cdf | 2,148,190 | openreview | https://github.com/DL4mHealth/LEAD | DL4mHealth/LEAD | ec35aadb1bc068fcacdcd8d036964514ca2a708f | repos/KuhCUX2oIt.zip | 1b5c8b68a7b4f426f3e86a2fe6af37791322193010525516be45fd384bdf1a08 | 3,205,704 | 114 | {
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} | 5,765 | {
"Jupyter Notebook": 22251065,
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} | false | 2026-04-01T07:40:39 | {
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3Wuvqc4xoy | 2,025 | rejected | Learning Efficient Representations of Neutrino Telescope Events | [
"Felix J. Yu",
"Nicholas Kamp",
"Carlos A. Argüelles"
] | [
"~Felix_J._Yu1",
"~Nicholas_Kamp1",
"~Carlos_A._Argüelles1"
] | OpenReview API | Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background inte... | Reject | 4 | [
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"rating": 3,
"soundness": 2,
"presentation": 1,
"contribution": 2,
"confidence": 4,
"summary": "This article presents an approach to learning representations of neutrino ... | https://openreview.net/forum?id=3Wuvqc4xoy | 2410.13148 | papers/3Wuvqc4xoy.pdf | b4a75aab91c7fe43593e28061037dd529d35043e8951855b5cdac92af7dfb66e | 3,815,067 | openreview | https://github.com/felixyu7/om2vec | felixyu7/om2vec | 60394e1dc5293126e32e4dab8510eca1f4fcbd12 | repos/3Wuvqc4xoy.zip | 0f36d3f6efe9befbc2d6b29ea75b5b6e6eeb9c482c1125084d1c1d8da82316a8 | 14,083 | 7 | {
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} | 344 | {
"Python": 31260
} | false | 2025-07-07T19:26:05 | {
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UTGv8CayNt | 2,024 | rejected | Chain-of-Thought Predictive Control | [
"Zhiwei Jia",
"Vineet Thumuluri",
"Fangchen Liu",
"Linghao Chen",
"Zhiao Huang",
"Hao Su"
] | [
"~Zhiwei_Jia1",
"~Vineet_Thumuluri1",
"~Fangchen_Liu2",
"~Linghao_Chen2",
"~Zhiao_Huang1",
"~Hao_Su1"
] | OpenReview API | We study generalizable policy learning from demonstrations for complex low-level control tasks (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learning method that utilizes scalable, albeit sub-optimal, demonstrations. Firstly, we propose an observation space-agnostic approach that ... | Reject | 4 | [
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"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "2 fair",
"contribution": "3 good",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=UTGv8CayNt | 2304.00776 | papers/UTGv8CayNt.pdf | a8356f018ce2525dd1ac4f5d8becf5410d6861b0a751cfd9439fe05d08a85fef | 2,263,379 | openreview | https://github.com/SeanJia/CoTPC | SeanJia/CoTPC | 1c971be6cd5bdbfd59e0ad5607be0d5c149b9b5b | repos/UTGv8CayNt.zip | fb46e2bfca37887a5954b748d773d02cce036b29201d5fe3f048218dbf28f824 | 215,039 | 13 | {
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alaQzRbCY9w | 2,022 | rejected | Bolstering Stochastic Gradient Descent with Model Building | [
"Ilker Birbil",
"Özgür Martin",
"Gönenc Onay",
"Figen Öztoprak"
] | [
"~Ilker_Birbil1",
"~Özgür_Martin1",
"~Gönenc_Onay1",
"~Figen_Öztoprak1"
] | OpenReview API | Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand. Although this tuning process can require large ... | Reject | null | 4 | [
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"confidence": "5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.",
"recommendat... | https://openreview.net/forum?id=alaQzRbCY9w | 2111.07058 | papers/alaQzRbCY9w.pdf | c8640f5e63b98f80e48a730ebf87250d83fe93a6f5750bca93e54fd843963406 | 1,577,973 | openreview | https://github.com/sibirbil/SMB | sibirbil/SMB | 17fb8ba4f440a5e36e66d701385ecdef6cb05723 | repos/alaQzRbCY9w.zip | fe6636bb802a0fefd225b034c8c5aaf15cc298ffaed48f9d5f501c79b7797f27 | 86,928 | 15 | {
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} | 2,671 | {
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} | false | 2023-02-15T16:44:03 | {
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XEw5Onu69uu | 2,021 | rejected | Self-Labeling of Fully Mediating Representations by Graph Alignment | [
"Martijn Oldenhof",
"Adam Arany",
"Yves Moreau",
"Jaak Simm"
] | [
"~Martijn_Oldenhof1",
"~Adam_Arany1",
"~Yves_Moreau2",
"~Jaak_Simm1"
] | OpenReview API | To be able to predict a molecular graph structure ($W$) given a 2D image of a chemical compound ($U$) is a challenging problem in machine learning. We are interested to learn $f: U \rightarrow W$ where we have a fully mediating representation $V$ such that $f$ factors into $U \rightarrow V \rightarrow W$. However, obs... | Reject | null | 4 | [
{
"id": "QYCxs3VLiPd",
"reviewer_signature": [
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],
"rating": "4: Ok but not good enough - rejection",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pre... | https://openreview.net/forum?id=XEw5Onu69uu | 2103.14133 | papers/XEw5Onu69uu.pdf | 11b887ee3ea1e57b05d84ba267dfda746ee2aaede94e6d5aa21e41f344f241ee | 1,208,307 | openreview | https://github.com/biolearning-stadius/chemgrapher-self-rich-labeling | biolearning-stadius/chemgrapher-self-rich-labeling | 7018a7d2ea0e9288dc94cdce5fb7024d4c14cd55 | repos/XEw5Onu69uu.zip | a5fd427ca5577951a8b26772e7ccb79c1c0f3e311811b6a5a21b412f8fa5f1f1 | 1,553,802 | 23 | {
".py": 22,
".ipynb": 1
} | 1,635 | {
"Jupyter Notebook": 185146,
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} | false | 2021-09-02T09:39:42 | {
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} | {
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} |
qiOIqQ9CwA | 2,026 | rejected | Optimal Stepsize for Diffusion Sampling | [
"Jianning Pei",
"Han Hu",
"Shuyang Gu"
] | [
"~Jianning_Pei1",
"~Han_Hu1",
"~Shuyang_Gu1"
] | OpenReview API | Diffusion models achieve remarkable generation quality but suffer from computational intensive sampling due to suboptimal step discretization. While existing works focus on optimizing denoising directions, we address the principled design of stepsize schedules. This paper proposes Optimal Stepsize Distillation, a dynam... | Reject | 3 | [
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"rating": 2,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper proposes a dynamic programming idea for obtaining the optimal ... | https://openreview.net/forum?id=qiOIqQ9CwA | 2503.21774 | papers/qiOIqQ9CwA.pdf | ade4e5a2ed53aff1db1fe45f258548b09292d60c941a17e739fde0381e5ee9dd | 43,910,505 | openreview | https://github.com/bebebe666/OptimalSteps | bebebe666/OptimalSteps | ee350436c86e29088c0cf550300a2e6a5911a7ee | repos/qiOIqQ9CwA.zip | 8a6e85af2d138a34cf05c7a4bc3fa53b1b4ac03a3c89140fd74e90305c7cfedc | 6,127,121 | 15 | {
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} | 5,993 | {
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} | false | 2025-04-13T05:54:03 | {
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} | {
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} | |
PIHPmNNp7w | 2,025 | rejected | Retrieval-Augmented Decision Transformer: External Memory for In-context RL | [
"Thomas Schmied",
"Fabian Paischer",
"Vihang Prakash Patil",
"Markus Hofmarcher",
"Razvan Pascanu",
"Sepp Hochreiter"
] | [
"~Thomas_Schmied1",
"~Fabian_Paischer1",
"~Vihang_Prakash_Patil1",
"~Markus_Hofmarcher1",
"~Razvan_Pascanu1",
"~Sepp_Hochreiter1"
] | OpenReview API | In-context learning (ICL) is the ability of a model to learn a new task by observing a few exemplars in its context. While prevalent in NLP, this capability has recently also been observed in Reinforcement Learning (RL) settings. Prior in-context RL methods, however, require entire episodes in the agent's context. Give... | Reject | 3 | [
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"rating": 8,
"soundness": 3,
"presentation": 4,
"contribution": 3,
"confidence": 4,
"summary": "This paper tackles the challenge of in-context learning in complex RL envi... | https://openreview.net/forum?id=PIHPmNNp7w | 2410.07071 | papers/PIHPmNNp7w.pdf | 1871449b61b208290f9be73ca01277721718faa459a8e1d3656a58d345234951 | 11,811,785 | openreview | https://github.com/ml-jku/RA-DT | ml-jku/RA-DT | 40adec5cc4a8f3aeef1e84c5a203eb55ebd9d481 | repos/PIHPmNNp7w.zip | e0b5ab37a38cbba26b08aaaa8b1019a34a49501f0f2bd6e2fe9ec95e7bb84ac1 | 384,060 | 75 | {
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} | 349 | {
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} | false | 2024-10-27T16:10:03 | {
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GY1fKFXG5i | 2,024 | rejected | Non-Vacuous Generalization Bounds for Large Language Models | [
"Sanae Lotfi",
"Marc Anton Finzi",
"Yilun Kuang",
"Tim G. J. Rudner",
"Micah Goldblum",
"Andrew Gordon Wilson"
] | [
"~Sanae_Lotfi1",
"~Marc_Anton_Finzi1",
"~Yilun_Kuang1",
"~Tim_G._J._Rudner2",
"~Micah_Goldblum1",
"~Andrew_Gordon_Wilson1"
] | OpenReview API | Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora. We provide the first non-vacuous generalization bounds for pretrained large language models (LLMs), indicating that language models are ca... | Reject | 5 | [
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"rating": "8: accept, good paper",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "3: You are fairly confident in your assessment. I... | https://openreview.net/forum?id=GY1fKFXG5i | 2312.17173 | papers/GY1fKFXG5i.pdf | 04d76647540f51fc646fc5a187ee916012d89d1963d8f7f1dcb99c89fc3c20e7 | 362,216 | openreview | https://github.com/Sanaelotfi/sublora-bounds-for-llms | Sanaelotfi/sublora-bounds-for-llms | c606ea664e54fe60cfd870167bf6d1c1183fedf1 | repos/GY1fKFXG5i.zip | 9e3f91b35553c7183df48379e9e9122da9c8283abe392b5447ee5eeca52c964f | 310,471 | 16 | {
".py": 16
} | 300 | {
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} | false | 2024-06-04T01:38:45 | {
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} | {
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} | |
cwiFbXPW4G0 | 2,023 | rejected | Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees | [
"Pengfei Li",
"Jianyi Yang",
"Shaolei Ren"
] | [
"~Pengfei_Li2",
"~Jianyi_Yang1",
"~Shaolei_Ren1"
] | OpenReview API | Many real-world problems, such as online ad display, can be formulated as online bipartite matching. The crucial challenge lies in the nature of sequentially-revealed online item information, based on which we make irreversible matching decisions at each step. While numerous expert online algorithms have been proposed ... | Reject | null | 3 | [
{
"id": "LYch1q2K2Gw",
"reviewer_signature": [
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],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=cwiFbXPW4G0 | 2306.00172 | papers/cwiFbXPW4G0.pdf | c22fb89087ec769907de6de169c2f567782c149838560417b3eec7e181246aea | 479,149 | openreview | https://github.com/Ren-Research/LOMAR | Ren-Research/LOMAR | 7710662a31b55fdafe1c542da1842d8ed9f2e7a9 | repos/cwiFbXPW4G0.zip | 705514101b0a81274b01b361c9136fb3914c41542aeb17c326c845caa01a2874 | 704,788 | 40 | {
".py": 40
} | 682 | {
"Python": 323617
} | false | 2023-08-09T02:45:38 | {
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} | {
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} |
8QE3pwEVc8P | 2,022 | rejected | Zero-Cost Operation Scoring in Differentiable Architecture Search | [
"Lichuan Xiang",
"Łukasz Dudziak",
"Mohamed S Abdelfattah",
"Thomas Chun Pong Chau",
"Nicholas Donald Lane",
"Hongkai Wen"
] | [
"~Lichuan_Xiang1",
"~Łukasz_Dudziak1",
"~Mohamed_S_Abdelfattah1",
"~Thomas_Chun_Pong_Chau1",
"~Nicholas_Donald_Lane1",
"~Hongkai_Wen1"
] | OpenReview API | Differentiable neural architecture search (NAS) has attracted significant attention in recent years due to its ability to quickly discover promising architectures of deep neural networks even in very large search spaces. Despite its success, many differentiable NAS methods lack robustness and may degenerate to trivial ... | Reject | null | 4 | [
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],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=8QE3pwEVc8P | 2106.06799 | papers/8QE3pwEVc8P.pdf | aa5dd9bcb28ebb7ec261ff973d60e9a01ecf2012c7287464d33f77e5417e77d0 | 1,426,415 | openreview | https://github.com/visionbasicagent/zerocost_operation_score | visionbasicagent/zerocost_operation_score | 55fc52b29a8d1be086937a7e8045d155ec0cf63f | repos/8QE3pwEVc8P.zip | 16e9163ccc8694ced2039ff210c27c2528c06074bd659e816b9498c1f2c8480c | 4,029,109 | 230 | {
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".sh": 94,
".ipynb": 13
} | 3,010 | {
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} | false | 2022-12-01T19:51:51 | {
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} | {
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fycxGdpCCmW | 2,021 | rejected | Hybrid Discriminative-Generative Training via Contrastive Learning | [
"Hao Liu",
"Pieter Abbeel"
] | [
"~Hao_Liu1",
"~Pieter_Abbeel2"
] | OpenReview API | Contrastive learning and supervised learning have both seen significant progress and success. However, thus far they have largely been treated as two separate objectives, brought together only by having a shared neural network. In this paper we show that through the perspective of hybrid discriminative-generative train... | Reject | null | 4 | [
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"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
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".py": 11,
".js": 5
} | 1,648 | {
"Python": 153412,
"JavaScript": 28156,
"HTML": 23946,
"CSS": 8270
} | false | 2023-05-01T20:42:33 | {
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4eYSSSDle6 | 2,026 | rejected | PRL: Prompts from Reinforcement Learning | [
"Paweł Batorski",
"Adrian Kosmala",
"Paul Swoboda"
] | [
"~Paweł_Batorski2",
"~Adrian_Kosmala1",
"~Paul_Swoboda1"
] | OpenReview API | Effective prompt engineering remains a central challenge in fully harnessing the
capabilities of LLMs. While well-designed prompts can dramatically enhance
performance, crafting them typically demands expert intuition and a nuanced understanding of the task. Moreover, the most impactful prompts often hinge on
subtle se... | Reject | 4 | [
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"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces PRL (Prompts from Reinforcement Learning), a reinfor... | https://openreview.net/forum?id=4eYSSSDle6 | 2505.14412 | papers/4eYSSSDle6.pdf | f1566a76074a2d37315d2af3a19028f167133afec792b8cdef1ff017cabaac61 | 380,024 | openreview | https://github.com/Batorskq/PRL-Prompts-from-Reinforcement-Learning | Batorskq/PRL-Prompts-from-Reinforcement-Learning | f9797cb987f5fd9f2ce5aa7b9e1e15b12f6ea328 | repos/4eYSSSDle6.zip | b1c37c1a30cb5a61d90d3ade1bb1af8207ed3e00720d5aacbd20ea2ff0bbf95f | 7,940,967 | 276 | {
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} | 6,953 | {
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} | false | 2025-06-05T11:48:47 | {
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O6W9SJRZRA | 2,025 | rejected | Right on Time: Revising Time Series Models by Constraining their Explanations | [
"Maurice Kraus",
"David Steinmann",
"Antonia Wüst",
"Andre Kokozinski",
"Kristian Kersting"
] | [
"~Maurice_Kraus1",
"~David_Steinmann1",
"~Antonia_Wüst1",
"~Andre_Kokozinski1",
"~Kristian_Kersting1"
] | OpenReview API | The reliability of deep time series models is often compromised by their tendency to rely on confounding factors, which may lead to incorrect outputs. Our newly recorded, naturally confounded dataset named P2S from a real mechanical production line emphasizes this. To avoid “Clever-Hans” moments in time series, i.e., t... | Reject | 4 | [
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"rating": 6,
"soundness": 4,
"presentation": 3,
"contribution": 3,
"confidence": 3,
"summary": "The paper proposes a right for the right reason method (RRR) for time ser... | https://openreview.net/forum?id=O6W9SJRZRA | 2402.12921 | papers/O6W9SJRZRA.pdf | 6bfb009dd3dc87b73bf435d1ed7fb5263e4730490588c417ad8ed4a8df0e50f4 | 2,753,655 | openreview | https://github.com/ml-research/RioT | ml-research/RioT | 0480158d53ec9b2218eec89d7b3f3f8f02277b09 | repos/O6W9SJRZRA.zip | 27d523b40012ccb9d5e532118e91723b54300dce2c644ac1c73196264aec2547 | 391,126 | 86 | {
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} | 354 | {
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} | false | 2024-06-18T15:51:51 | {
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VmqTuFMk68 | 2,024 | rejected | Trainable Transformer in Transformer | [
"Abhishek Panigrahi",
"Sadhika Malladi",
"Mengzhou Xia",
"Sanjeev Arora"
] | [
"~Abhishek_Panigrahi1",
"~Sadhika_Malladi2",
"~Mengzhou_Xia1",
"~Sanjeev_Arora1"
] | OpenReview API | Recent works attribute the capability of in-context learning (ICL) in large pre-trained language models to implicitly simulating and fine-tuning an internal model (e.g., linear or 2-layer MLP) during inference. However, such constructions require large memory overhead, which makes simulation of more sophisticated inter... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "3 good",
"presentation": "2 fair",
"contribution": "3 good",
"confidence": "2: You are willing to defe... | https://openreview.net/forum?id=VmqTuFMk68 | 2307.01189 | papers/VmqTuFMk68.pdf | 98ec028b07033358362f20d42a8140dfdfa00362f3773c28f8de52652c3832c1 | 3,692,509 | openreview | https://github.com/abhishekpanigrahi1996/transformer_in_transformer | abhishekpanigrahi1996/transformer_in_transformer | 1722081db59c1a9459941ec9e2c2f039fba9f331 | repos/VmqTuFMk68.zip | 5121d3b818a7748a5094d969bb0fc6da3d10596fd5a03d42b0ce550559403ea6 | 321,987 | 40 | {
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} | 315 | {
"Python": 500283,
"Shell": 3505
} | false | 2023-10-11T17:55:19 | {
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} | {
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QEmn_Hvh7j8 | 2,023 | rejected | Private GANs, Revisited | [
"Alex Bie",
"Gautam Kamath",
"Guojun Zhang"
] | [
"~Alex_Bie1",
"~Gautam_Kamath1",
"~Guojun_Zhang1"
] | OpenReview API | We show that with improved training, the standard approach for differentially private GANs -- updating the discriminator with noisy gradients -- achieves or competes with state-of-the-art results for private image synthesis. Existing instantiations of this approach neglect to consider how adding noise only to discrimin... | Reject | null | 3 | [
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} | 710 | {
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} | false | 2023-10-05T14:00:31 | {
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2PSrjVtj6gU | 2,022 | rejected | Graph Attention Multi-layer Perceptron | [
"Wentao Zhang",
"Ziqi Yin",
"Zeang Sheng",
"Yang Li",
"Wen Ouyang",
"Xiaosen Li",
"Yangyu Tao",
"Zhi Yang",
"Bin CUI"
] | [
"~Wentao_Zhang1",
"~Ziqi_Yin1",
"~Zeang_Sheng1",
"~Yang_Li36",
"~Wen_Ouyang1",
"~Xiaosen_Li1",
"~Yangyu_Tao2",
"~Zhi_Yang4",
"~Bin_CUI2"
] | OpenReview API | Recently, graph neural networks (GNNs) have achieved a stride of success in many graph-based applications. However, most GNNs suffer from a critical issue: representation learned is constructed based on a fixed k-hop neighborhood and insensitive to individual needs for each node, which greatly hampers the performance o... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=2PSrjVtj6gU | 2206.04355 | papers/2PSrjVtj6gU.pdf | f0060f3ef66ee90a5ddf5e29fd6c06411e50d4e370c739f7482748e6af33faa0 | 700,295 | openreview | https://github.com/PKU-DAIR/GAMLP | PKU-DAIR/GAMLP | 83dc5d4a414c1829ed4a793410d3bd6037ee89ee | repos/2PSrjVtj6gU.zip | cc28d0919c20311ff05a335c267ee9523e5b8a83df9ea94be48f684d4f5e30be | 1,183,856 | 9 | {
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} | 3,027 | {
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} | false | 2022-06-25T09:04:23 | {
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nCY83KxoehA | 2,021 | rejected | Automated Concatenation of Embeddings for Structured Prediction | [
"Xinyu Wang",
"Yong Jiang",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Kewei Tu"
] | [
"~Xinyu_Wang3",
"~Yong_Jiang1",
"~Nguyen_Bach1",
"~Tao_Wang4",
"~Zhongqiang_Huang1",
"~Fei_Huang2",
"~Kewei_Tu1"
] | OpenReview API | Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depe... | Reject | null | 4 | [
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],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"so... | https://openreview.net/forum?id=nCY83KxoehA | 2010.05006 | papers/nCY83KxoehA.pdf | a3c69d41fa029be962e56d93214e2fc09181deabb5f0be33fc1c7bfb04ffd848 | 401,053 | openreview | https://github.com/Alibaba-NLP/ACE | Alibaba-NLP/ACE | cf50440b5d4ab44f74feb4214733224265428db4 | repos/nCY83KxoehA.zip | 91055b0b5bac3f17a3e0649024ed0f846fe80507facaa1a22a9bff8164a4e169 | 1,456,656 | 123 | {
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} | 1,758 | {
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} | false | 2022-12-02T09:13:18 | {
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} | {
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AEgyitdRWf | 2,026 | rejected | ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering | [
"Zexi Liu",
"Jingyi Chai",
"Xinyu Zhu",
"Shuo Tang",
"Rui Ye",
"Weiyu Ma",
"Bo Zhang",
"LEI BAI",
"Siheng Chen"
] | [
"~Zexi_Liu1",
"~Jingyi_Chai1",
"~Xinyu_Zhu5",
"~Shuo_Tang2",
"~Rui_Ye1",
"~Weiyu_Ma1",
"~Bo_Zhang17",
"~LEI_BAI1",
"~Siheng_Chen1"
] | OpenReview API | The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-based paradigm exhibits limitations: smaller models lack the capacity to learn from execution trajectories for generalization, while large pro... | Reject | 4 | [
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"rating": 4,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper proposes an online reinforcement learning agent training framew... | https://openreview.net/forum?id=AEgyitdRWf | 2505.23723 | papers/AEgyitdRWf.pdf | 860c4bcd5f977fd96dee280d1b269e40187a1e98d91ba4605c4080df351f5137 | 2,392,506 | openreview | https://github.com/MASWorks/ML-Agent | MASWorks/ML-Agent | 15932e7525deb99d59f7416bbe8c75077cff3690 | repos/AEgyitdRWf.zip | c0207c1ddb4cf5870bdda0284dfbfcac5ec13fa7a44cc5c4e0ed04a15d175414 | 7,311,896 | 156 | {
".py": 150,
".sh": 6
} | 7,394 | {
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} | false | 2025-06-21T18:17:12 | {
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f4mQ2SU5tp | 2,025 | rejected | IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models | [
"Hang Guo",
"Yawei Li",
"Tao Dai",
"Shu-Tao Xia",
"Luca Benini"
] | [
"~Hang_Guo3",
"~Yawei_Li1",
"~Tao_Dai3",
"~Shu-Tao_Xia1",
"~Luca_Benini2"
] | OpenReview API | Fine-tuning large-scale text-to-image diffusion models for various downstream tasks has yielded impressive results. However, the heavy computational burdens of tuning large models prevent personal customization. Recent advances have attempted to employ parameter-efficient fine-tuning (PEFT) techniques to adapt the floa... | Reject | 4 | [
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"rating": 5,
"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "The authors propose IntLoRA, which employes INT low-rank parameters to ada... | https://openreview.net/forum?id=f4mQ2SU5tp | 2410.21759 | papers/f4mQ2SU5tp.pdf | 492452adf5049baefb33b9651cbcacfebb16a8a6dfe5b92d6b8ab6c43ff447f0 | 4,691,337 | openreview | https://github.com/csguoh/IntLoRA | csguoh/IntLoRA | 65a8257a4311e0feab9b33477c9748d13d8ce17b | repos/f4mQ2SU5tp.zip | 10ce454be8b0b948d631cf377c4b144f9adeca2d32df503b960660c75ac62578 | 367,037 | 9 | {
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} | 358 | {
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"Shell": 8177
} | false | 2024-11-25T04:23:17 | {
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17ZbByq95E | 2,024 | rejected | Memory-Efficient Backpropagation through Large Linear Layers | [
"Daniel Bershatsky",
"Aleksandr Mikhalev",
"Aleksandr Katrutsa",
"Julia Gusak",
"Daniil Merkulov",
"Ivan Oseledets"
] | [
"~Daniel_Bershatsky1",
"~Aleksandr_Mikhalev1",
"~Aleksandr_Katrutsa1",
"~Julia_Gusak1",
"~Daniil_Merkulov1",
"~Ivan_Oseledets1"
] | OpenReview API | In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to perform backpropagation through linear layers. Since the gradients of linear layers are computed by matrix multiplications, we consider meth... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=17ZbByq95E | 2201.13195 | papers/17ZbByq95E.pdf | 74f1ef669098cb715ce131858a8bbe5267936d9ea86d47a41cbcd8b4fac2f10a | 474,137 | openreview | https://github.com/skolai/fewbit | skolai/fewbit | 940706bbbc40b11ade61d42253f80dc960d35793 | repos/17ZbByq95E.zip | 330303eaa470a820abfde80e7eb67e3d37feaa9b41e71eef065f95da6ef2d0e8 | 310,980 | 53 | {
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} | 321 | {
"Python": 141112,
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"Cuda": 30862,
"Shell": 5429,
"CMake": 3968,
"Dockerfile": 1542
} | false | 2023-07-26T04:42:05 | {
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zHSaBQtj-l | 2,023 | rejected | Differentiable Rendering with Reparameterized Volume Sampling | [
"Kirill Struminsky",
"Oleg Desheulin"
] | [
"~Kirill_Struminsky1",
"~Oleg_Desheulin2"
] | OpenReview API | We propose an alternative rendering algorithm for neural radiance fields based on importance sampling. In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of views of a scene. To generate a pixel of a novel view, it marches a ray through the pixel and com... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=zHSaBQtj-l | 2302.10970 | papers/zHSaBQtj-l.pdf | d297a60270bccf9ea8cfdc0f012983c1d59f68dcb49de45bc14c247569dcf531 | 4,336,943 | openreview | https://github.com/GreatDrake/reparameterized-volume-sampling | GreatDrake/reparameterized-volume-sampling | 1fc6979f465e0e556786eee36648c7231c4006a9 | repos/zHSaBQtj-l.zip | ead7086e7d32d43e88d4251c3bb2c08321eb3b1f3a18d2840208909b03c444f9 | 703,419 | 9 | {
".py": 8,
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} | 713 | {
"Jupyter Notebook": 603423,
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} | false | 2024-04-21T13:44:00 | {
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8nXkyH2_s6 | 2,021 | rejected | Neural networks behave as hash encoders: An empirical study | [
"Fengxiang He",
"Shiye Lei",
"Jianmin Ji",
"Dacheng Tao"
] | [
"~Fengxiang_He1",
"leishiye@gmail.com",
"jianmin@ustc.edu.cn",
"~Dacheng_Tao1"
] | OpenReview API | The input space of a neural network with ReLU-like activations is partitioned into multiple linear regions, each corresponding to a specific activation pattern of the included ReLU-like activations. We demonstrate that this partition exhibits the following encoding properties across a variety of deep learning models: (... | Reject | null | 4 | [
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"rating": "5: Marginally below acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"s... | https://openreview.net/forum?id=8nXkyH2_s6 | 2101.05490 | papers/8nXkyH2_s6.pdf | e07a0c4bf55da59616a0fcf40bc32c09a0b9cb779bb1670aeaa8ba51badf72cb | 1,089,361 | openreview | https://github.com/LeavesLei/activation-code | LeavesLei/activation-code | 6be962f5e6c081d0bbefd6a30f31b13728da7752 | repos/8nXkyH2_s6.zip | a4bc5085e3e930bca96833b326175109900c7f7e546e7aebd615e73d37bdf373 | 810,980 | 136 | {
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} | 1,795 | {
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} | false | 2021-05-28T08:55:34 | {
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Xq4NdAodoA | 2,026 | rejected | Generative Modeling with Bayesian Sample Inference | [
"Marten Lienen",
"Marcel Kollovieh",
"Stephan Günnemann"
] | [
"~Marten_Lienen1",
"~Marcel_Kollovieh1",
"~Stephan_Günnemann1"
] | OpenReview API | We derive a novel generative model from iterative Gaussian posterior inference. By treating the generated sample as an unknown variable, we can formulate the sampling process in the language of Bayesian probability. Our model uses a sequence of prediction and posterior update steps to iteratively narrow down the unknow... | Reject | 4 | [
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"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 4,
"summary": "The paper introduces Bayesian Sample Inference (BSI), a generative framew... | https://openreview.net/forum?id=Xq4NdAodoA | 2502.07580 | papers/Xq4NdAodoA.pdf | 45bc0fe5d84ca629ba40629a7afd658a34a9e54b0740dedb4aeee71a1b489ce8 | 1,576,494 | openreview | https://github.com/martenlienen/bsi | martenlienen/bsi | 5b8a6acf17f2c7106d6fa31a474738e3b412c9c1 | repos/Xq4NdAodoA.zip | 00094761be0e38c281c9f17c770f2ff23a01f14944dbb71a63401fb7d9083827 | 7,537,638 | 49 | {
".py": 48,
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} | 7,457 | {
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} | false | 2026-08-14T05:54:45 | {
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e3odKmatZr | 2,025 | rejected | Critique-out-Loud Reward Models | [
"Zachary Ankner",
"Mansheej Paul",
"Brandon Cui",
"Jonathan Daniel Chang",
"Prithviraj Ammanabrolu"
] | [
"~Zachary_Ankner1",
"~Mansheej_Paul1",
"~Brandon_Cui1",
"~Jonathan_Daniel_Chang1",
"~Prithviraj_Ammanabrolu1"
] | OpenReview API | Traditionally, reward models used for reinforcement learning from human feedback (RLHF) are trained to directly predict preference scores without leveraging the generation capabilities of the underlying large language model (LLM). This limits the capabilities of reward models as they must reason implicitly about the qu... | Reject | 4 | [
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"rating": 3,
"soundness": 2,
"presentation": 4,
"contribution": 2,
"confidence": 4,
"summary": "This paper proposes a reward modeling approach that combines next-token-pr... | https://openreview.net/forum?id=e3odKmatZr | 2408.11791 | papers/e3odKmatZr.pdf | 7a128b93c833ac7cfc77cf2cbba0d1770f1c289609ec2b549cfc84625fe53bc3 | 1,763,330 | openreview | https://github.com/zankner/CLoud | zankner/CLoud | fac417e2c0f45fab9083bf9b77066de490e8288c | repos/e3odKmatZr.zip | da1b3911374b086d2d2cb64f352ce2ca1fa5847fb6d598cfb3d7e113429a8e24 | 378,121 | 16 | {
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} | 362 | {
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} | false | 2024-10-18T19:38:52 | {
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01LMSeReNvY | 2,023 | rejected | PromptBoosting: Black-Box Text Classification with Ten Forward Passes | [
"Bairu Hou",
"Joe O'Connor",
"Jacob Andreas",
"Shiyu Chang",
"Yang Zhang"
] | [
"~Bairu_Hou2",
"~Joe_O'Connor1",
"~Jacob_Andreas1",
"~Shiyu_Chang2",
"~Yang_Zhang3"
] | OpenReview API | We describe PromptBoosting, a query-efficient procedure for building a text classifier from a neural language model (LM) without access to the LM’s parameters, gradients, or hidden representations. This form of “black-box” classifier training has become increasingly important as the cost of training and inference in la... | Reject | null | 4 | [
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"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=01LMSeReNvY | 2212.09257 | papers/01LMSeReNvY.pdf | a7492816417762dbfd110f87eb6b6cbf4b16e9d7c1806461fa8e949b8ed458be | 700,916 | openreview | https://github.com/UCSB-NLP-Chang/PromptBoosting | UCSB-NLP-Chang/PromptBoosting | 6843af7da67a27f21f1de76950a7f0c0f3fb7785 | repos/01LMSeReNvY.zip | df0b24f71dc2ce1c47abf0ac5eb5f215ae73dbdd41bb8600db3ebb40c36f3a11 | 1,088,188 | 23 | {
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} | 747 | {
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} | false | 2023-09-05T19:08:49 | {
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FOR2VqgJXb | 2,021 | rejected | Evaluating representations by the complexity of learning low-loss predictors | [
"William F Whitney",
"Min Jae Song",
"David Brandfonbrener",
"Jaan Altosaar",
"Kyunghyun Cho"
] | [
"~William_F_Whitney1",
"~Min_Jae_Song1",
"~David_Brandfonbrener1",
"~Jaan_Altosaar1",
"~Kyunghyun_Cho1"
] | OpenReview API | We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on a task of interest. To this end, we introduce two measures: surplus d... | Reject | null | 3 | [
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"rating": "4: Ok but not good enough - rejection",
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vnlsxFbWSB | 2,026 | rejected | Bench-CoE: A Framework for Collaboration of Experts from Benchmark | [
"Yuanshuai Wang",
"Xingjian Zhang",
"Jinkun Zhao",
"Siwei Wen",
"Peilin Feng",
"Shuhao Liao",
"Lei Huang",
"wenjun wu"
] | [
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"~Xingjian_Zhang2",
"~Jinkun_Zhao1",
"~Siwei_Wen5",
"~Peilin_Feng1",
"~Shuhao_Liao2",
"~Lei_Huang1",
"~wenjun_wu3"
] | OpenReview API | Large Language Models (LLMs) are key technologies that drive intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven experts with diverse capabilities have been developed, spreading from language to visual understanding and generalization, accompanied by c... | Reject | 5 | [
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"confidence": 3,
"summary": "This paper proposes Bench-CoE, a framework for collaborating multiple exp... | https://openreview.net/forum?id=vnlsxFbWSB | 2412.04167 | papers/vnlsxFbWSB.pdf | 6f392a8c90df25218c53f6852fb37d0ced17942cf7d9b0f9f2c6b2b1887eb19b | 1,047,702 | openreview | https://github.com/ZhangXJ199/Bench-CoE | ZhangXJ199/Bench-CoE | fab309d6c397e92d60907ec345a8989043afa04c | repos/vnlsxFbWSB.zip | e28ddcbfbb200506ef93fa136458dc675b2b54a97e0d069b0fb483806dc6bab4 | 7,402,808 | 127 | {
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} | false | 2025-04-27T05:44:00 | {
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j9wBgcxa7N | 2,025 | rejected | MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning | [
"Justin Chen",
"Archiki Prasad",
"Swarnadeep Saha",
"Elias Stengel-Eskin",
"Mohit Bansal"
] | [
"~Justin_Chen1",
"~Archiki_Prasad1",
"~Swarnadeep_Saha2",
"~Elias_Stengel-Eskin1",
"~Mohit_Bansal2"
] | OpenReview API | Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples for each problem and aggregating over them to find a better answer. While these improve performance, they often reach a saturation point beyond which additional samples provide no return. Ref... | Reject | 5 | [
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"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces MAGICORE, an inference framework for LLM reasoning ... | https://openreview.net/forum?id=j9wBgcxa7N | 2409.12147 | papers/j9wBgcxa7N.pdf | 9845342614e4bfeb7d8cd67c48a4fea6f5b086f81f5505a26cf94c973efb14bf | 929,378 | openreview | https://github.com/dinobby/MAgICoRE | dinobby/MAgICoRE | 1388006b309663da69b142102d819d3be4d34890 | repos/j9wBgcxa7N.zip | de87d379bd0bb1ca31f895558bf5c3450f82737d25a5de7242f137690d3b266e | 306,128 | 8 | {
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} | 365 | {
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} | false | 2024-09-19T06:17:44 | {
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0aEUd9UtiA | 2,024 | rejected | DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning | [
"Longxiang He",
"Linrui Zhang",
"Junbo Tan",
"Xueqian Wang"
] | [
"~Longxiang_He2",
"~Linrui_Zhang1",
"~Junbo_Tan1",
"~Xueqian_Wang1"
] | OpenReview API | Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous methods may still encounter out-of-distribution actions due to the limited expressivity of Gaussian-based policies. On the other hand, directly... | Reject | 3 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "2 fair",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=0aEUd9UtiA | 2310.05333 | papers/0aEUd9UtiA.pdf | 22c566f0eb1b3e96ce15bf9a5f87e9c6540f3de10d854215ef528987d829f16a | 2,207,264 | openreview | https://github.com/felix-thu/DiffCPS | felix-thu/DiffCPS | 2d8de2878bdc2bb2f59bbb9f7ce929897cf7cb15 | repos/0aEUd9UtiA.zip | 3f7a18f629a0c2ad024c463a3080b848785bffd3208d275cdfd4a5f3d19db200 | 332,031 | 11 | {
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} | 329 | {
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} | false | 2024-09-09T16:28:24 | {
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YjKqWExiy6s | 2,023 | rejected | Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization | [
"Runqi Lin",
"Chaojian Yu",
"Tongliang Liu"
] | [
"~Runqi_Lin1",
"~Chaojian_Yu1",
"~Tongliang_Liu1"
] | OpenReview API | Single-step adversarial training (SSAT) is shown to be able to defend against iterative-step adversarial attacks to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO) with strong adversaries, showing that the classifier decision boundaries are highly distorted and robust ac... | Reject | null | 4 | [
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} | 767 | {
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} | false | 2025-02-15T07:05:41 | {
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9Cwxjd6nRh | 2,022 | rejected | High Fidelity Visualization of What Your Self-Supervised Representation Knows About | [
"Florian Bordes",
"Randall Balestriero",
"Pascal Vincent"
] | [
"~Florian_Bordes1",
"~Randall_Balestriero1",
"~Pascal_Vincent1"
] | OpenReview API | Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of how much information is contained in the representation of a... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=9Cwxjd6nRh | 2112.09164 | papers/9Cwxjd6nRh.pdf | e2819039495fa338837ee48e0f35ca1bb67dbdb02f8d45d0cca8106991b64b60 | 44,596,143 | openreview | https://github.com/facebookresearch/RCDM | facebookresearch/RCDM | 71daaf10a73bb2012864f0827c68d209fc92b0a5 | repos/9Cwxjd6nRh.zip | 4778ee2854148947cb035b8eb56a6e92c898e8db70648271145fb82e02e8b9c2 | 3,425,002 | 28 | {
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} | 3,345 | {
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} | true | 2023-05-03T20:06:06 | {
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ohdw3t-8VCY | 2,021 | rejected | CTRLsum: Towards Generic Controllable Text Summarization | [
"Junxian He",
"Wojciech Maciej Kryscinski",
"Bryan McCann",
"Nazneen Rajani",
"Caiming Xiong"
] | [
"~Junxian_He1",
"~Wojciech_Maciej_Kryscinski1",
"~Bryan_McCann1",
"~Nazneen_Rajani1",
"~Caiming_Xiong1"
] | OpenReview API | Current summarization systems yield generic summaries that are disconnected from users' preferences and expectations. To address this limitation, we present CTRLsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the su... | Reject | null | 4 | [
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... | https://openreview.net/forum?id=ohdw3t-8VCY | 2012.04281 | papers/ohdw3t-8VCY.pdf | 7df38c89ff91288cef0ac6eb33396b248fb88620209b215048792980dddb3d44 | 387,961 | openreview | https://github.com/salesforce/ctrl-sum | salesforce/ctrl-sum | 01c1a6cf0e286346321829ea88d30c85fbede0ca | repos/ohdw3t-8VCY.zip | 32126739d7e095fbd94c9be1414ee068b5334bfc5025cecf6029354994e8d363 | 1,831,724 | 23 | {
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YBgjDBYPzz | 2,026 | rejected | Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding | [
"Konstantin Berestizshevsky",
"Renzo Andri",
"Lukas Cavigelli"
] | [
"~Konstantin_Berestizshevsky1",
"~Renzo_Andri1",
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] | OpenReview API | We present Top-Theta (Top-θ) Attention, a training-free method for sparsifying transformer attention during inference. Our key insight is that static, per-head thresholds can be calibrated to retain the desired constant number of significant elements per attention row. This approach enables content-based sparsity witho... | Reject | 3 | [
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"rating": 8,
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"contribution": 3,
"confidence": 4,
"summary": "The paper proposes Top-Theta Attention, a training-free sparsification met... | https://openreview.net/forum?id=YBgjDBYPzz | 2502.08363 | papers/YBgjDBYPzz.pdf | 26fdc80d8cfeb1fc700da2f9aaffb44a0c5a4f84628ac1acf7ea523970f00ebf | 4,913,761 | openreview | https://github.com/huawei-csl/top-theta-attention | huawei-csl/top-theta-attention | fa3ab6889137994cd393162ff8236993f3391053 | repos/YBgjDBYPzz.zip | 0debb1e23d3363604f7f959602e361dffd9739123b68c86f148e94f70266926d | 5,863,761 | 25 | {
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} | false | 2026-06-04T11:37:22 | {
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OANUpvmnuf | 2,025 | rejected | Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration | [
"Yun Qu",
"Boyuan Wang",
"Yuhang Jiang",
"Jianzhun Shao",
"Yixiu Mao",
"Chang Liu",
"Cheems Wang",
"Xiangyang Ji"
] | [
"~Yun_Qu2",
"~Boyuan_Wang1",
"~Yuhang_Jiang3",
"~Jianzhun_Shao1",
"~Yixiu_Mao2",
"~Chang_Liu9",
"~Cheems_Wang1",
"~Xiangyang_Ji1"
] | OpenReview API | With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning.
Although pursuing novelty, diversity, or uncertainty attracts increasing attention, redundant efforts brought by exploration without proper guidance choices poses a practical issue for the c... | Reject | 4 | [
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"soundness": 3,
"presentation": 3,
"contribution": 2,
"confidence": 3,
"summary": "This paper study utilizing LLMs to improve exploration for multi-agent RL ... | https://openreview.net/forum?id=OANUpvmnuf | 2410.02511 | papers/OANUpvmnuf.pdf | cc3a22bb34cd614ab808aa296535ef3e270687752fd0c153436cad4d978bea5b | 6,752,884 | openreview | https://github.com/hijkzzz/pymarl2 | hijkzzz/pymarl2 | 8ccac7c5aa134422a2e3009be735d23cdf8ce2f8 | repos/OANUpvmnuf.zip | 240e13e2e93cc3360a458d7589b872e6143e2424c3160892b3bcb2288ac8057a | 350,311 | 81 | {
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} | 373 | {
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} | false | 2024-05-18T03:22:54 | {
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q38SZkUmUh | 2,024 | rejected | FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation | [
"Tu Vu",
"Mohit Iyyer",
"Xuezhi Wang",
"Noah Constant",
"Jerry Wei",
"Jason Wei",
"Chris Tar",
"Yun-Hsuan Sung",
"Denny Zhou",
"Quoc V Le",
"Thang Luong"
] | [
"~Tu_Vu1",
"~Mohit_Iyyer1",
"~Xuezhi_Wang3",
"~Noah_Constant1",
"~Jerry_Wei1",
"~Jason_Wei1",
"ctar@google.com",
"~Yun-Hsuan_Sung1",
"~Denny_Zhou1",
"~Quoc_V_Le1",
"~Thang_Luong1"
] | OpenReview API | Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we ... | Reject | 3 | [
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"soundness": "3 good",
"presentation": "3 good",
"contribution": "3 good",
"confidence": "4: You are confident in yo... | https://openreview.net/forum?id=q38SZkUmUh | 2310.03214 | papers/q38SZkUmUh.pdf | 65c3af0884306b6f5d750991b09bb4f6c1b6f6846917abf32af4009193544643 | 2,172,992 | openreview | https://github.com/freshllms/freshqa | freshllms/freshqa | 7d2d3683991916f3633e480548a6aa5c9a62e3db | repos/q38SZkUmUh.zip | 7eb5ace9d7d9af0a39c1b3e4cf3e90c7f87bee1c39c1b1e61543e54c7ec03bee | 39,840 | 3 | {
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} | false | 2026-05-01T22:43:08 | {
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qhplAU1BOZW | 2,023 | rejected | Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge | [
"Sara Babakniya",
"Souvik Kundu",
"Saurav Prakash",
"Yue Niu",
"Salman Avestimehr"
] | [
"~Sara_Babakniya1",
"~Souvik_Kundu2",
"~Saurav_Prakash1",
"~Yue_Niu1",
"~Salman_Avestimehr1"
] | OpenReview API | Limited computation and communication capabilities of clients pose significant challenges in federated learning (FL) over resource-limited edge nodes. A potential solution to this problem is to deploy off-the-shelf sparse learning algorithms that train a binary sparse mask on each client with the expectation of trainin... | Reject | null | 3 | [
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"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piece... | https://openreview.net/forum?id=qhplAU1BOZW | 2208.13092 | papers/qhplAU1BOZW.pdf | 88534b715314720914f09d213eea012d2e13dde7fbf7f5c6beff0521bf7d95b9 | 16,715,668 | openreview | https://github.com/SaraBabakN/flash_fl | SaraBabakN/flash_fl | fb883133c48490e7d4e3f21cce4212a27f618a22 | repos/qhplAU1BOZW.zip | 976ce2eb95014d02e09f63ed5d9f3c4a4b3a58971bc6ffb89ddc176e701b4e7a | 219,002 | 13 | {
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} | false | 2023-11-04T07:06:51 | {
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u2JeVfXIQa | 2,022 | rejected | Adaptive Cross-Layer Attention for Image Restoration | [
"Yancheng Wang",
"Yingzhen Yang",
"Chong Chen",
"Ning Xu"
] | [
"~Yancheng_Wang2",
"~Yingzhen_Yang1",
"chongchen@kuaishou.com",
"~Ning_Xu3"
] | OpenReview API | Non-local attention module has been proven to be crucial for image restoration. Conventional non-local attention processes features of each layer separately, so it risks missing correlation between features among different layers. To address this problem, we propose Cross-Layer Attention (CLA) module in this paper. Ins... | Reject | null | 4 | [
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission... | https://openreview.net/forum?id=u2JeVfXIQa | 2203.03619 | papers/u2JeVfXIQa.pdf | f0a2f5a94fab68e20d1b7273dd910d759ca4cf17af372ffa9be9d2d07658ded8 | 1,851,316 | openreview | https://github.com/Statistical-Deep-Learning/ACLA-IKS | Statistical-Deep-Learning/ACLA-IKS | aaefb77dcc44ba81d06c13b3d12db2b641c15273 | repos/u2JeVfXIQa.zip | 3978e80dada063057fd8071aaf83a086103c758d428e01b6cda787227e2a7847 | 3,785,892 | 160 | {
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} | false | 2025-11-06T23:50:10 | {
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nPVlVsBTiJ | 2,021 | rejected | Adversarial Boot Camp: label free certified robustness in one epoch | [
"Ryan Campbell",
"Chris Finlay",
"Adam M Oberman"
] | [
"~Ryan_Campbell2",
"~Chris_Finlay1",
"~Adam_M_Oberman1"
] | OpenReview API | Machine learning models are vulnerable to adversarial attacks. One approach to addressing this vulnerability is certification, which focuses on models that are guaranteed to be robust for a given perturbation size. A drawback of recent certified models is that they are stochastic: they require multiple computationall... | Reject | null | 4 | [
{
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],
"rating": "3: Clear rejection",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pre... | https://openreview.net/forum?id=nPVlVsBTiJ | 2010.02508 | papers/nPVlVsBTiJ.pdf | f9ca0805c0c46466ecec5c73facc7a969fec08078581600b7d57376d44843cf8 | 785,534 | openreview | https://github.com/ryancampbell514/HeatSmoothing | ryancampbell514/HeatSmoothing | a67b3d857ea6bb37c34c6ce591f7626052278035 | repos/nPVlVsBTiJ.zip | f03dd8d49c3585c0ceef82f4cddec9c186a02444c496b4e312a3d494c5aeb38c | 562,492 | 52 | {
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} | 1,988 | {
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} | false | 2020-10-08T17:32:43 | {
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} | {
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} |
RYwtJyOP3k | 2,026 | rejected | Probably Approximately Correct Labels | [
"Emmanuel Candes",
"Andrew Ilyas",
"Tijana Zrnic"
] | [
"~Emmanuel_Candes1",
"~Andrew_Ilyas1",
"~Tijana_Zrnic1"
] | OpenReview API | Obtaining high-quality labeled datasets is often costly, requiring either
human annotation or expensive experiments.
In theory, powerful pre-trained AI models provide an opportunity to
automatically label datasets and save costs.
Unfortunately, these models provide no guarantees on their accuracy,
making wholesale ... | Reject | 4 | [
{
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"ICLR.cc/2026/Conference/Submission17773/Reviewer_FoeC"
],
"rating": 4,
"soundness": 3,
"presentation": 4,
"contribution": 2,
"confidence": 3,
"summary": "This paper introduces Probably Approximately Correct (PAC) Labeling, aimi... | https://openreview.net/forum?id=RYwtJyOP3k | 2506.10908 | papers/RYwtJyOP3k.pdf | f0e07a02481cec4b5b435452eb445f152afc99067218518f8d89203bffc1fbb0 | 1,030,000 | openreview | https://github.com/tijana-zrnic/pac-labels | tijana-zrnic/pac-labels | b415b58756b14b384529ac9cf146bd5d4c8139aa | repos/RYwtJyOP3k.zip | 38020d18668956f268bd2520397262c99c7ed7858bec0e9a31160cbcbf512dbc | 12,267,016 | 6 | {
".ipynb": 3,
".py": 3
} | 7,615 | {
"Jupyter Notebook": 28007,
"Python": 20683
} | false | 2025-06-13T06:30:15 | {
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"pwc_url": "https://paperswithcode.com/paper/probably-approximately-correct-labels"
} | {
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} | |
leSbzBtofH | 2,025 | rejected | AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses | [
"Nicholas Carlini",
"Edoardo Debenedetti",
"Javier Rando",
"Milad Nasr",
"Florian Tramèr"
] | [
"~Nicholas_Carlini1",
"~Edoardo_Debenedetti1",
"~Javier_Rando2",
"~Milad_Nasr2",
"~Florian_Tramèr1"
] | OpenReview API | We introduce AutoAdvExBench, a benchmark to evaluate if large language models (LLMs)
can autonomously exploit defenses to adversarial examples.
We believe our benchmark will be valuable to several distinct audiences.
First, it measures if models can match the abilities of expert adversarial machine learning researcher... | Reject | 6 | [
{
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"ICLR.cc/2025/Conference/Submission14023/Reviewer_G63v"
],
"rating": 8,
"soundness": 4,
"presentation": 4,
"contribution": 3,
"confidence": 3,
"summary": "This paper proposes a new benchmark to test LLM capabilities: whether the... | https://openreview.net/forum?id=leSbzBtofH | 2503.01811 | papers/leSbzBtofH.pdf | 3dbbb97e83361880472f876f10dd59a6ada4d4c4f1e0068eab4276bd9ec1dea3 | 250,543 | openreview | https://github.com/ethz-spylab/autoadvexbench | ethz-spylab/autoadvexbench | 8da21de3aa3442b339b519a0f5c82b54905efbf1 | repos/leSbzBtofH.zip | 79ec0284af77407ac1cdfda75e7e82e5b0743baafc953763d99a853a743c6e06 | 397,012 | 32 | {
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".js": 1
} | 374 | {
"Python": 93972,
"HTML": 21117
} | false | 2025-05-21T18:36:47 | {
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} | |
XJiN1VkgA0 | 2,024 | rejected | Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models | [
"Zhen Lin",
"Shubhendu Trivedi",
"Jimeng Sun"
] | [
"~Zhen_Lin2",
"~Shubhendu_Trivedi2",
"~Jimeng_Sun3"
] | OpenReview API | Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains. However, gauging the trustworthiness of responses generated by LLMs remains an open challenge, with limited research on uncertainty quantification (UQ) for ... | Reject | 4 | [
{
"id": "dJgUfjK6Y4",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission8527/Reviewer_1J6D"
],
"rating": "6: marginally above the acceptance threshold",
"soundness": "3 good",
"presentation": "2 fair",
"contribution": "3 good",
"confidence": "3: You are fairly confiden... | https://openreview.net/forum?id=XJiN1VkgA0 | 2305.19187 | papers/XJiN1VkgA0.pdf | 6fe887b68b1544c5ac4a5f576b66d4939c7ec6e27360566492527c8d9a52fcb0 | 711,403 | openreview | https://github.com/zlin7/UQ-NLG | zlin7/UQ-NLG | ecaea91741a6c1076e9069b56d32a9929eea8ff9 | repos/XJiN1VkgA0.zip | b8914f75d45f8be4a7828b14affe9f54ec8a93cf61ec67c5f864569714605a56 | 346,125 | 18 | {
".py": 16,
".ipynb": 2
} | 345 | {
"Jupyter Notebook": 262628,
"Python": 97829
} | false | 2024-06-30T21:12:23 | {
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} | {
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} | |
pcBJT4bgbpH | 2,023 | rejected | Attention Flows for General Transformers | [
"Niklas Metzger",
"Christopher Hahn",
"Julian Siber",
"Frederik Schmitt",
"Bernd Finkbeiner"
] | [
"~Niklas_Metzger1",
"~Christopher_Hahn1",
"~Julian_Siber1",
"~Frederik_Schmitt1",
"~Bernd_Finkbeiner1"
] | OpenReview API | In this paper, we study the computation of how much an input token in a Transformer model influences its prediction. We formalize a method to construct a flow network out of the attention values of encoder-only Transformer models and extend it to general Transformer architectures, including an auto-regressive decoder. ... | Reject | null | 4 | [
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],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piece... | https://openreview.net/forum?id=pcBJT4bgbpH | 2205.15389 | papers/pcBJT4bgbpH.pdf | 8e3695108d43694082a958ca8e0c65057752efe072b63ed8bf478366b2821839 | 8,711,270 | openreview | https://github.com/reactive-systems/ml2 | reactive-systems/ml2 | 33d9696c94de6d27aa836ae8118118a7277ff35c | repos/pcBJT4bgbpH.zip | 0c7557ed1e26ad1408b973c8a1f0e8f05314334a3d659f408e52f6f83f174a4a | 556,930 | 436 | {
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} | 833 | {
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"Dockerfile": 15364,
"Shell": 2114
} | false | 2025-04-01T14:13:17 | {
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} | {
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} |
TQ75Md-FqQp | 2,022 | rejected | Efficient and Modular Implicit Differentiation | [
"Mathieu Blondel",
"Quentin Berthet",
"marco cuturi",
"Roy Frostig",
"Stephan Hoyer",
"Felipe Llinares-López",
"Fabian Pedregosa",
"Jean-Philippe Vert"
] | [
"~Mathieu_Blondel1",
"~Quentin_Berthet2",
"~marco_cuturi2",
"~Roy_Frostig1",
"~Stephan_Hoyer1",
"~Felipe_Llinares-López1",
"~Fabian_Pedregosa1",
"~Jean-Philippe_Vert1"
] | OpenReview API | Automatic differentiation (autodiff) has revolutionized machine learning. It allows expressing complex computations by composing elementary ones in creative ways and removes the tedious burden of computing their derivatives by hand. More recently, differentiation of optimization problem solutions has attracted a great... | Reject | null | 3 | [
{
"id": "IwKMNAl6nic",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper4362/Reviewer_Q3Lr"
],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=TQ75Md-FqQp | 2105.15183 | papers/TQ75Md-FqQp.pdf | c80fc1805f72a0ce8f7321dc71ace7191bda8ab64b2d513f37abfdf1e27a52f5 | 2,202,952 | openreview | https://github.com/google/jaxopt | google/jaxopt | 176b13830bc552236b32a4c3c4654f196c8b4cd6 | repos/TQ75Md-FqQp.zip | 97c9a4807ae55bc66f2027544a5f2d6e295aa18a7b5d0e49c9d7edf12986feb2 | 2,247,572 | 126 | {
".py": 118,
".ipynb": 8
} | 3,535 | {
"Python": 922179
} | false | 2026-09-07T21:43:54 | {
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"pwc_url": "https://paperswithcode.com/paper/efficient-and-modular-implicit"
} | {
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} |
5wmNjjvGOXh | 2,021 | rejected | Selfish Sparse RNN Training | [
"Shiwei Liu",
"Decebal Constantin Mocanu",
"Yulong Pei",
"Mykola Pechenizkiy"
] | [
"~Shiwei_Liu2",
"~Decebal_Constantin_Mocanu1",
"~Yulong_Pei1",
"~Mykola_Pechenizkiy1"
] | OpenReview API | Sparse neural networks have been widely applied to reduce the necessary resource requirements to train and deploy over-parameterized deep neural networks. For inference acceleration, methods that induce sparsity from a pre-trained dense network (dense-to-sparse) work effectively. Recently, dynamic sparse training (DST)... | Reject | null | 4 | [
{
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],
"rating": "7: Good paper, accept",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"presentation": "",
... | https://openreview.net/forum?id=5wmNjjvGOXh | 2101.09048 | papers/5wmNjjvGOXh.pdf | 8d88788cb8f686585bc754a7b77b04ccd1adf48f5b65a4e949d3d0417c3ad671 | 1,853,573 | openreview | https://github.com/Shiweiliuiiiiiii/Selfish-RNN | Shiweiliuiiiiiii/Selfish-RNN | 06b03db04bfe93f20f8fc703e9a78a83cea777a7 | repos/5wmNjjvGOXh.zip | 0504ce22b9e498a9509623020f599bba977ede7efdc5b9201b8fde7f52123e55 | 2,046,934 | 13 | {
".py": 13
} | 2,101 | {
"Python": 104692
} | false | 2021-10-08T03:52:04 | {
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"pwc_url": "https://paperswithcode.com/paper/selfish-sparse-rnn-training-1"
} | {
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} |
uwoA5iyTC6 | 2,026 | rejected | Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs | [
"Yucong Luo",
"Yitong Zhou",
"Mingyue Cheng",
"Jiahao Wang",
"Daoyu Wang"
] | [
"~Yucong_Luo1",
"~Yitong_Zhou2",
"~Mingyue_Cheng1",
"~Jiahao_Wang25",
"~Daoyu_Wang1"
] | OpenReview API | To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures. Despite their effectiveness, most existing methods still adhere to a fast thinking paradigm - relying on pattern recognition and t... | Reject | 4 | [
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"rating": 4,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 3,
"summary": "This paper proposes Time-R1, a two-stage reinforcement fine-tuning (RFT) ... | https://openreview.net/forum?id=uwoA5iyTC6 | 2506.10630 | papers/uwoA5iyTC6.pdf | e71338135a187cf3ecf4d2440013466d2798916226d2f56fec55e3c53ad8c1ad | 6,664,272 | openreview | https://github.com/ustc-time-series/Time-R1 | ustc-time-series/Time-R1 | 2cd666eaddac37734b45103d654b6d2d22bf23ed | repos/uwoA5iyTC6.zip | b16dc8a9161bbb16427a6e4d162446b69d6f827a345bba5fd99e1b53a634b135 | 7,496,202 | 259 | {
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} | 7,771 | {
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} | false | 2026-04-14T08:32:23 | {
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} | {
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pVL4bYKOGM | 2,025 | rejected | Conformal prediction for causal effects of continuous treatments | [
"Maresa Schröder",
"Dennis Frauen",
"Jonas Schweisthal",
"Konstantin Hess",
"Valentyn Melnychuk",
"Stefan Feuerriegel"
] | [
"~Maresa_Schröder1",
"~Dennis_Frauen1",
"~Jonas_Schweisthal1",
"~Konstantin_Hess1",
"~Valentyn_Melnychuk1",
"~Stefan_Feuerriegel1"
] | OpenReview API | Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effe... | Reject | 4 | [
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"rating": 6,
"soundness": 3,
"presentation": 3,
"contribution": 3,
"confidence": 2,
"summary": "This paper proposes a conformal prediction method for continuous treatment... | https://openreview.net/forum?id=pVL4bYKOGM | 2407.03094 | papers/pVL4bYKOGM.pdf | 6e0fe044a34f2471fc6cc544cf6f01e0748186b0e043027539398867ee64b184 | 1,848,233 | openreview | https://github.com/m-schroder/ContinuousCausalCP | m-schroder/ContinuousCausalCP | d557555d6bb38239809b2c4d846c2768e9a279b7 | repos/pVL4bYKOGM.zip | 8e999ad3baa99b7926d1c2268094e4f43dd6cbd3c351846b9fd04d06103de02a | 96,126 | 9 | {
".py": 9
} | 374 | {
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} | false | 2024-07-04T11:28:49 | {
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} | {
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npf3gREtf7 | 2,024 | rejected | Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection | [
"Costas Mavromatis",
"Balasubramaniam Srinivasan",
"Zhengyuan Shen",
"Jiani Zhang",
"Huzefa Rangwala",
"Christos Faloutsos",
"George Karypis"
] | [
"~Costas_Mavromatis1",
"~Balasubramaniam_Srinivasan1",
"~Zhengyuan_Shen1",
"~Jiani_Zhang2",
"~Huzefa_Rangwala2",
"~Christos_Faloutsos1",
"~George_Karypis1"
] | OpenReview API | Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is efficient as it does not require any parameter updates to the trained LLM, but only few annotated examples as input for the LLM. In this work, we investigate an active learning approach for ICL, where there is a limited budget for... | Reject | 4 | [
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"rating": "3: reject, not good enough",
"soundness": "2 fair",
"presentation": "3 good",
"contribution": "1 poor",
"confidence": "4: You are confident in your assessment, but... | https://openreview.net/forum?id=npf3gREtf7 | 2310.20046 | papers/npf3gREtf7.pdf | 3a2c953209a509c4d3882001a9bf84bb744ad7e4246ababaa33a0eeda22bdeb6 | 1,364,854 | openreview | https://github.com/amazon-science/adaptive-in-context-learning | amazon-science/adaptive-in-context-learning | d0ea1c7d333517de592b4c1bf3862ab2ddfa027a | repos/npf3gREtf7.zip | 2dbbfd33c298268ae85ac003d2ebd7988b899983ea75beb21986e42e30d990ed | 374,045 | 13 | {
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} | 354 | {
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} | true | 2023-10-30T20:14:11 | {
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} | {
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MtGmCCPJD- | 2,023 | rejected | Repository-Level Prompt Generation for Large Language Models of Code | [
"Disha Shrivastava",
"Hugo Larochelle",
"Daniel Tarlow"
] | [
"~Disha_Shrivastava1",
"~Hugo_Larochelle1",
"~Daniel_Tarlow1"
] | OpenReview API | With the success of large language models (LLMs) of code and their use as code assistants (e.g.\ Codex used in GitHub Copilot, techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a framework called Repo-Level Prompt Generator that learns to genera... | Reject | null | 4 | [
{
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"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=MtGmCCPJD- | 2206.12839 | papers/MtGmCCPJD-.pdf | 2d88ae7c3f1764c4ba10c08def0acb0edc46217ab8b3b3dc2fc55ac169f42238 | 509,561 | openreview | https://github.com/shrivastavadisha/repo_level_prompt_generation | shrivastavadisha/repo_level_prompt_generation | 3af5f3424740448d8e325b3726e61944f6eec8b6 | repos/MtGmCCPJD-.zip | d220dde993f61f5712b042b3f1077b29abdb317a01728c84e79a48276920b50d | 459,465 | 21 | {
".py": 21
} | 865 | {
"Python": 150598
} | false | 2023-04-22T15:23:11 | {
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} | {
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G-7GlfTneYg | 2,022 | rejected | VoiceFixer: Toward General Speech Restoration with Neural Vocoder | [
"Haohe Liu",
"Qiuqiang Kong",
"Qiao Tian",
"Yan Zhao",
"DeLiang Wang",
"Chuanzeng Huang",
"Yuxuan Wang"
] | [
"~Haohe_Liu1",
"~Qiuqiang_Kong1",
"~Qiao_Tian1",
"~Yan_Zhao6",
"~DeLiang_Wang1",
"~Chuanzeng_Huang1",
"~Yuxuan_Wang1"
] | OpenReview API | Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping. However, SSR systems only focus on one task and do not address the general speech restoration problem. In addition, previous SSR systems sho... | Reject | null | 4 | [
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jYkO_0z2TAr | 2,021 | rejected | Zero-Shot Learning with Common Sense Knowledge Graphs | [
"Nihal Nayak",
"Stephen Bach"
] | [
"~Nihal_Nayak1",
"~Stephen_Bach1"
] | OpenReview API | Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples. We propose to learn class representations from common sense knowledge graphs. Common sense knowledge graphs are an untapped source of explicit high-level ... | Reject | null | 3 | [
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"rating": "4: Ok but not good enough - rejection",
"confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature",
"re... | https://openreview.net/forum?id=jYkO_0z2TAr | 2006.10713 | papers/jYkO_0z2TAr.pdf | 4e858af206ee3c02b4724cc720e1600cfdbad41c109938ba1b32dafd77d88ae4 | 490,474 | openreview | https://github.com/BatsResearch/nayak-tmlr22-code | BatsResearch/nayak-tmlr22-code | fd86c1d2dfb2dd540fd744ce8c49bf58719abadf | repos/jYkO_0z2TAr.zip | 3cabbebf859e82a84ee72c4ba043d96edfca5aacfbc7698b95174201b9e6b70a | 2,280,638 | 55 | {
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} | 2,212 | {
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} | false | 2022-07-25T11:24:21 | {
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3DZeEUTwhq | 2,026 | rejected | Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs | [
"Will Cai",
"Tianneng Shi",
"Xuandong Zhao",
"Dawn Song"
] | [
"~Will_Cai1",
"~Tianneng_Shi1",
"~Xuandong_Zhao1",
"~Dawn_Song1"
] | OpenReview API | Commercial Large Language Model (LLM) APIs create a fundamental trust problem: users pay for specific models but have no guarantee that providers deliver them faithfully. Providers may covertly substitute cheaper alternatives (e.g., quantized versions, smaller models) to reduce costs while maintaining advertised pricin... | Reject | 4 | [
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"presentation": 2,
"contribution": 1,
"confidence": 4,
"summary": "This paper studies the problem of auditing model substitution in LLM APIs.... | https://openreview.net/forum?id=3DZeEUTwhq | 2504.04715 | papers/3DZeEUTwhq.pdf | d1d2e34ab04395637bdf86f1e4148b3dcc2003f87913e613caa9b313cc7810ea | 688,098 | openreview | https://github.com/sunblaze-ucb/llm-api-audit | sunblaze-ucb/llm-api-audit | 21ffeef65c4c92689e4db5a6a8ec2a779d2bc049 | repos/3DZeEUTwhq.zip | ec41d15b8c3142121872c13b845e74f8d6377f973d8ca42663456af21a085add | 11,433,031 | 409 | {
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} | 7,772 | {
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} | false | 2025-04-10T06:22:13 | {
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uLAAVg0ymc | 2,025 | rejected | Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants | [
"Abdoulaye SAKHO",
"Emmanuel Malherbe",
"Erwan Scornet"
] | [
"~Abdoulaye_SAKHO1",
"~Emmanuel_Malherbe3",
"~Erwan_Scornet1"
] | OpenReview API | Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In this paper, we prove that SMOTE (with default parameter) tends to copy the original minority samples asymptotically. We also prove that SMOTE ... | Reject | 4 | [
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"rating": 3,
"soundness": 2,
"presentation": 2,
"contribution": 2,
"confidence": 4,
"summary": "This paper makes a theretical analysis of the well-known SMOTE method for ... | https://openreview.net/forum?id=uLAAVg0ymc | 2402.03819 | papers/uLAAVg0ymc.pdf | 534af05494452613ed9d4eb4c133140dc887364d31ed35afa35e5b8740f70947 | 530,463 | openreview | https://github.com/artefactory/smote_strategies_study | artefactory/smote_strategies_study | 476f902a7aafbea99432d0ac5de95509e2ff275a | repos/uLAAVg0ymc.zip | 9fc8edb25052a82b46e9453ef84830192bd0043d71d931030e4e9e502614727c | 143,969 | 10 | {
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} | 375 | {
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} | false | 2025-09-02T11:37:07 | {
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K7KQkiHanD | 2,024 | rejected | One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning | [
"Arnav Chavan",
"Zhuang Liu",
"Deepak Gupta",
"Eric Xing",
"Zhiqiang Shen"
] | [
"~Arnav_Chavan1",
"~Zhuang_Liu1",
"~Deepak_Gupta2",
"~Eric_Xing1",
"~Zhiqiang_Shen1"
] | OpenReview API | We present Generalized LoRA (GLoRA), a flexible approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate activations, providing more flexibility and capability across divers... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "4 excellent",
"presentation": "4 excellent",
"contribution": "3 good",
"confidence": "5: You are absol... | https://openreview.net/forum?id=K7KQkiHanD | 2306.07967 | papers/K7KQkiHanD.pdf | fe6fd2a0e19a44a4b39b4fb2ade6c68b50e2e87f99bf25b6d0ab7f357ddc586e | 786,191 | openreview | https://github.com/Arnav0400/ViT-Slim | Arnav0400/ViT-Slim | 390467ddf529d747168709ce2f007a85a0399cca | repos/K7KQkiHanD.zip | 046de9581cfd6cd723aeec9fd014f3bc55f6f862c9970e3585046ec4e695998c | 352,907 | 22 | {
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} | 367 | {
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} | false | 2025-08-24T17:10:22 | {
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ESR6hysKDsW | 2,023 | rejected | Class-Incremental Learning with Repetition | [
"Hamed Hemati",
"Andrea Cossu",
"Antonio Carta",
"Julio Hurtado",
"Lorenzo Pellegrini",
"Davide Bacciu",
"Vincenzo Lomonaco",
"Damian Borth"
] | [
"~Hamed_Hemati1",
"~Andrea_Cossu1",
"~Antonio_Carta1",
"~Julio_Hurtado1",
"~Lorenzo_Pellegrini1",
"~Davide_Bacciu1",
"~Vincenzo_Lomonaco1",
"~Damian_Borth1"
] | OpenReview API | Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike replay, cannot be controlled by the user. Nowadays, Class-Incremental scenarios represent the leading test-bed for assessing and comparing CL... | Reject | null | 3 | [
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"confidence": "2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar w... | https://openreview.net/forum?id=ESR6hysKDsW | 2301.11396 | papers/ESR6hysKDsW.pdf | 1e207e1d3488917781efede8cde237c154479521c6254024c9734223b9d14d53 | 1,545,202 | openreview | https://github.com/HamedHemati/CIR | HamedHemati/CIR | ca3305aba37e08ed1067f0da4847dba247c1569d | repos/ESR6hysKDsW.zip | 8f99cff9675234bee1bd98559a4c68b9e043863f6d4decee5920f01f2c496e04 | 829,247 | 47 | {
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} | 877 | {
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} | true | 2024-02-15T13:11:42 | {
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tJCwZBHm-jW | 2,022 | rejected | Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models | [
"Chenfeng Xu",
"Shijia Yang",
"Bohan Zhai",
"Bichen Wu",
"Xiangyu Yue",
"Wei Zhan",
"Peter Vajda",
"Kurt Keutzer",
"Masayoshi Tomizuka"
] | [
"~Chenfeng_Xu1",
"~Shijia_Yang1",
"~Bohan_Zhai1",
"~Bichen_Wu1",
"~Xiangyu_Yue1",
"~Wei_Zhan2",
"~Peter_Vajda1",
"~Kurt_Keutzer3",
"~Masayoshi_Tomizuka2"
] | OpenReview API | 3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed for 2D image and 3D point-cloud understanding are quite different.
Our paper explores the potential for transferring between these two represe... | Reject | null | 5 | [
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"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=tJCwZBHm-jW | 2106.04180 | papers/tJCwZBHm-jW.pdf | 03bd1b1923a6b2f9b09a42124957ccb97ee999331e20e2d49d59ad4f01879307 | 8,133,728 | openreview | https://github.com/chenfengxu714/image2point | chenfengxu714/image2point | 6efba318bcd4316dbe91e3a0daec6d8c9769cc8f | repos/tJCwZBHm-jW.zip | eb71a80727a67584ccc039dfcf8fbe20bfe0e3281b7c4648a3697fb9e3487b27 | 647,661 | 27 | {
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} | 4,153 | {
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} | false | 2022-11-16T23:56:47 | {
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Ns8v4jHGyAV | 2,021 | rejected | Matrix Shuffle-Exchange Networks for Hard 2D Tasks | [
"Emīls Ozoliņš",
"Karlis Freivalds",
"Agris Šostaks"
] | [
"~Emīls_Ozoliņš1",
"~Karlis_Freivalds1",
"agris.sostaks@lumii.lv"
] | OpenReview API | Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently ex... | Reject | null | 3 | [
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"rating": "4: Ok but not good enough - rejection",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"pre... | https://openreview.net/forum?id=Ns8v4jHGyAV | 2006.15892 | papers/Ns8v4jHGyAV.pdf | 9136d045c232fdb281cbe7a75fb2c890965df6d7e516c48cc8284355f5ecd69f | 530,449 | openreview | https://github.com/LUMII-Syslab/Matrix-SE | LUMII-Syslab/Matrix-SE | f398589d23d973836b78cf7dd5cf0872bff42f9c | repos/Ns8v4jHGyAV.zip | bc0c75dd22d020a8a06a52ae8e85e45b7bb7507701f01342f15218377210b614 | 469,630 | 26 | {
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} | 2,526 | {
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} | false | 2021-02-03T09:21:45 | {
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DdHrylM8Tr | 2,026 | rejected | AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models | [
"Jinchuan Zhang",
"Lu Yin",
"Yan Zhou",
"Songlin Hu"
] | [
"~Jinchuan_Zhang1",
"~Lu_Yin7",
"~Yan_Zhou8",
"~Songlin_Hu2"
] | OpenReview API | The emergence of agentic capabilities in large language models fundamentally transforms their risk profile from passive information providers to autonomous action executors, introducing unprecedented safety challenges that existing alignment methods fail to address. Current approaches lack systematic frameworks for und... | Reject | 3 | [
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"rating": 2,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 5,
"summary": "The authors introduce AgentAlign, a framework that models malicious agent ... | https://openreview.net/forum?id=DdHrylM8Tr | 2505.23020 | papers/DdHrylM8Tr.pdf | 25f041c7e9edc5ae49d76dbc7c1318b3b3a4ed4484996d5ffe15b374702b1cb5 | 601,537 | openreview | https://github.com/jc-ryan/AgentAlign | jc-ryan/AgentAlign | efb728f7211163a39eacdeab77b4d4e0618a5bc7 | repos/DdHrylM8Tr.zip | c25f2b40b8789c2f7c666427112b43ee0e6164f1b4bbed7f6c1e325f9698cc0f | 15,115,598 | 24 | {
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} | 7,934 | {
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} | false | 2025-06-02T04:42:57 | {
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WkpqUVcSTy | 2,025 | rejected | SlowFast-LLaVA: A strong training-free baseline for video large language models | [
"Mingze Xu",
"Mingfei Gao",
"Zhe Gan",
"Hong-You Chen",
"Zhengfeng Lai",
"Haiming Gang",
"Kai Kang",
"Afshin Dehghan"
] | [
"~Mingze_Xu2",
"~Mingfei_Gao1",
"~Zhe_Gan1",
"~Hong-You_Chen1",
"~Zhengfeng_Lai1",
"~Haiming_Gang1",
"~Kai_Kang2",
"~Afshin_Dehghan5"
] | OpenReview API | We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture the detailed spatial semantics and long-range temporal context without exceeding the token budget of commonly used LLMs. This is realized by using a two-stream SlowFast design of inputs for Video... | Reject | 4 | [
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"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "This paper proposes a training free method that converts an image MLLM into... | https://openreview.net/forum?id=WkpqUVcSTy | 2407.15841 | papers/WkpqUVcSTy.pdf | aab8f27afbd00e073502aa56244af6190f70832ebf3507cd5c363979e1296cc8 | 2,435,222 | openreview | https://github.com/apple/ml-slowfast-llava | apple/ml-slowfast-llava | bea0f73c106b91404ef403353e184b278fcc64a7 | repos/WkpqUVcSTy.zip | 58950ff192384e32750ad4f3698b8a232189e8020ae4ef3227b9ec21e8c678c4 | 422,991 | 52 | {
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XqLcFMMwNb | 2,024 | rejected | MM-LDM: Multi-Modal Latent Diffusion Model for Sounding Video Generation | [
"Mingzhen Sun",
"Weining Wang",
"Yanyuan Qiao",
"Longteng Guo",
"Jiahui Sun",
"Xinxin Zhu",
"Jing Liu"
] | [
"~Mingzhen_Sun1",
"~Weining_Wang3",
"~Yanyuan_Qiao1",
"~Longteng_Guo1",
"~Jiahui_Sun2",
"~Xinxin_Zhu1",
"~Jing_Liu1"
] | OpenReview API | Sounding video generation (SVG) is a challenging audio-video joint generation task that requires both single-modal realism and cross-modal consistency.
Previous diffusion-based methods tackled SVG within the original signal space, resulting in a huge computation burden.
In this paper, we introduce a novel multi-modal l... | Reject | 4 | [
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"rating": "5: marginally below the acceptance threshold",
"soundness": "2 fair",
"presentation": "1 poor",
"contribution": "2 fair",
"confidence": "4: You are confident in you... | https://openreview.net/forum?id=XqLcFMMwNb | 2410.01594 | papers/XqLcFMMwNb.pdf | 2839738e9086a2bd55bf678ca29282ed4eef5f72ab62d796a7dd725431bba470 | 3,860,841 | openreview | https://github.com/mzsun01/MM-LDM | mzsun01/MM-LDM | 5db569aef6d3f637022a2dc6b7947cbc016f01c4 | repos/XqLcFMMwNb.zip | 7352b51b13776acf37ab580ccde85174c8434b87a219786a402aba4e20ab32e9 | 271,400 | 43 | {
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} | false | 2024-04-12T07:34:16 | {
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HZJje06x6IO | 2,023 | rejected | Global Context Vision Transformers | [
"Ali Hatamizadeh",
"Hongxu Yin",
"Jan Kautz",
"Pavlo Molchanov"
] | [
"~Ali_Hatamizadeh1",
"~Hongxu_Yin2",
"~Jan_Kautz1",
"~Pavlo_Molchanov1"
] | OpenReview API | We propose global context vision transformer (GC ViT), a novel architecture that enhances parameter and compute utilization for computer vision tasks. The core of the novel model are global context self-attention modules, joint with standard local self-attention, to effectively yet efficiently model both long and shor... | Reject | null | 4 | [
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],
"rating": "",
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission ... | https://openreview.net/forum?id=HZJje06x6IO | 2206.09959 | papers/HZJje06x6IO.pdf | 0b7b5944d09a2db51ae20680ee0b5b3206a78a56f7241bf25ca1ac7288bd3d03 | 892,774 | openreview | https://github.com/NVlabs/GCVit | NVlabs/GCVit | 8e1941f2099eeb99ee9e8472a5ddbd6b94a8958f | repos/HZJje06x6IO.zip | 82adf96e1c063295ebc79d12403e52ac0d85f027928269464b3682a3852bef2d | 636,848 | 29 | {
".py": 27,
".sh": 2
} | 879 | {
"Python": 198144,
"Shell": 541
} | false | 2023-12-22T13:04:04 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/global-context-vision-transformers"
} | {
"paper_reviews_decision": "OpenReview API",
"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
in1ynkrXyMH | 2,022 | rejected | Introspective Learning : A Two-Stage approach for Inference in Neural Networks | [
"Mohit Prabhushankar",
"Ghassan AlRegib"
] | [
"~Mohit_Prabhushankar1",
"~Ghassan_AlRegib1"
] | OpenReview API | In this paper, we advocate for two stages in a neural network's decision making process. The first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. The second stage is a slower reflection stage where we ask the network to reflect o... | Reject | null | 4 | [
{
"id": "ZJ08YDHdcht",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper3981/Reviewer_yYCU"
],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=in1ynkrXyMH | 2209.08425 | papers/in1ynkrXyMH.pdf | d8bbd42572fa5fa1d3142db44be8a2ca2e130ad0b512f3aba86bd218fe0d69d2 | 6,856,615 | openreview | https://github.com/olivesgatech/Introspective-Learning | olivesgatech/Introspective-Learning | fd9a358f45d7b417d1687d3bcdcce18facba2b2a | repos/in1ynkrXyMH.zip | e2b0417600deb54e46f3d5fcb6b1f10cd27197ea91f6e2ec521ce7c869e9f238 | 4,259,454 | 3 | {
".py": 3
} | 4,165 | {
"Python": 24392
} | false | 2022-09-26T13:40:21 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/introspective-learning-a-two-stage-approach-1"
} | {
"paper_reviews_decision": "OpenReview API",
"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
GVNGAaY2Dr1 | 2,021 | rejected | Multi-Agent Collaboration via Reward Attribution Decomposition | [
"Tianjun Zhang",
"Huazhe Xu",
"Xiaolong Wang",
"Yi Wu",
"Kurt Keutzer",
"Joseph E. Gonzalez",
"Yuandong Tian"
] | [
"~Tianjun_Zhang1",
"~Huazhe_Xu1",
"~Xiaolong_Wang3",
"~Yi_Wu1",
"~Kurt_Keutzer1",
"~Joseph_E._Gonzalez1",
"~Yuandong_Tian1"
] | OpenReview API | Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-of-magnitude more training rounds than humans and don't generalize to new agent configurations even on the same game. In this work, we propo... | Reject | null | 4 | [
{
"id": "rabHEzOzQQL",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper2134/AnonReviewer1"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct",
"recommendation": "",
"s... | https://openreview.net/forum?id=GVNGAaY2Dr1 | 2010.08531 | papers/GVNGAaY2Dr1.pdf | af3be00a2a42879548e1f83bb21506e9282fd1eacc565b8ecdd0de404fbe59a8 | 3,480,548 | openreview | https://github.com/facebookresearch/CollaQ | facebookresearch/CollaQ | ac43314fcf955a21fd21cf644bea864bf4b5013a | repos/GVNGAaY2Dr1.zip | a93407365f39234746ece9400acaa03da19de4ddb6dd41122ac88cc52cfa6158 | 470,533 | 7 | {
".py": 7
} | 2,578 | {
"Python": 124283
} | true | 2023-08-14T21:56:29 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/multi-agent-collaboration-via-reward-1"
} | {
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"paper_code_mapping": "Papers With Code archive",
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} |
VD7GaNY1tJ | 2,026 | rejected | Robustly Improving LLM Fairness in Realistic Settings via Interpretability | [
"Adam Karvonen",
"Samuel Marks"
] | [
"~Adam_Karvonen1",
"~Samuel_Marks1"
] | OpenReview API | Large language models (LLMs) are increasingly deployed in high-stakes hiring applications, making decisions that directly impact people's careers and livelihoods. While prior studies suggest simple anti-bias prompts can eliminate demographic biases in controlled evaluations, we find these mitigations fail when realisti... | Reject | 3 | [
{
"id": "pRTBJkNgex",
"reviewer_signature": [
"ICLR.cc/2026/Conference/Submission20181/Reviewer_aZFN"
],
"rating": 2,
"soundness": 1,
"presentation": 1,
"contribution": 1,
"confidence": 4,
"summary": "The authors study the fairness of LLMs in the context of screening candid... | https://openreview.net/forum?id=VD7GaNY1tJ | 2506.10922 | papers/VD7GaNY1tJ.pdf | 108fc54bdbe9240959f5c11a2bc8c4bcfadc28a3504aefc740780cc8bf177985 | 481,133 | openreview | https://github.com/adamkarvonen/llm_bias | adamkarvonen/llm_bias | 11ce50baccd7fdf4f8eca6936a5e8682a5df4c1b | repos/VD7GaNY1tJ.zip | 2a1229891c5e75001cc47e715f36fe7af4a910f797d563810c49376e6cce0add | 8,546,158 | 35 | {
".py": 27,
".ipynb": 8
} | 8,359 | {
"Python": 277378,
"Jupyter Notebook": 217687
} | false | 2025-10-03T15:14:53 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/robustly-improving-llm-fairness-in-realistic"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
DQfHkEcUqV | 2,025 | rejected | Learning Extrapolative Sequence Transformations from Markov Chains | [
"Sophia Hager",
"Aleem Khan",
"Andrew Wang",
"Nicholas Andrews"
] | [
"~Sophia_Hager1",
"~Aleem_Khan1",
"~Andrew_Wang3",
"~Nicholas_Andrews2"
] | OpenReview API | Most successful applications of deep learning involve similar training and test conditions. However, for some generative tasks, samples should improve desirable properties beyond previously known values, which requires the ability to generate novel hypotheses that extrapolate beyond training data. While large language ... | Reject | 4 | [
{
"id": "bxYoyJzbbF",
"reviewer_signature": [
"ICLR.cc/2025/Conference/Submission11835/Reviewer_iCrL"
],
"rating": 5,
"soundness": 2,
"presentation": 3,
"contribution": 2,
"confidence": 4,
"summary": "This paper proposes a method for learning sample-efficient extrapolative ... | https://openreview.net/forum?id=DQfHkEcUqV | 2505.20251 | papers/DQfHkEcUqV.pdf | 35d14ebf9a3beec549c00b78a9ac00f3651b1b269837fb3bc091747148d218ba | 354,935 | openreview | https://github.com/sophia-hager/learning-MCMC-extrapolation | sophia-hager/learning-MCMC-extrapolation | 1692bbd894c0a9b226888006f2b1e67e95c10229 | repos/DQfHkEcUqV.zip | 9177f4d2078fda4565068800ead9be9264b524d1fbc3e1d899c13ffe45f20216 | 416,653 | 43 | {
".py": 35,
".sh": 8
} | 388 | {
"Python": 221029,
"Shell": 2777
} | false | 2025-08-07T23:26:25 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/learning-extrapolative-sequence"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
RxhOEngX8s | 2,024 | rejected | Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection | [
"Charles Guille-Escuret",
"Pierre-Andre Noel",
"Ioannis Mitliagkas",
"David Vazquez",
"Joao Monteiro"
] | [
"~Charles_Guille-Escuret1",
"~Pierre-Andre_Noel1",
"~Ioannis_Mitliagkas1",
"~David_Vazquez1",
"~Joao_Monteiro1"
] | OpenReview API | Deployed machine learning systems can be improved using methods detecting out-of-distribution (OOD) inputs. Existing research mainly focuses on one type of distribution shift: detecting samples from novel classes, absent from the training set. However, real-world systems encounter a broad variety of anomalous inputs, a... | Reject | 4 | [
{
"id": "BWD3y6yps6",
"reviewer_signature": [
"ICLR.cc/2024/Conference/Submission6028/Reviewer_WvA2"
],
"rating": "3: reject, not good enough",
"soundness": "3 good",
"presentation": "3 good",
"contribution": "1 poor",
"confidence": "2: You are willing to defend your assessment... | https://openreview.net/forum?id=RxhOEngX8s | 2308.11480 | papers/RxhOEngX8s.pdf | 0d1b319ec90c1738e8473cef1b3bf4c7f17f021c3203106db75ea2b769c19338 | 2,040,858 | openreview | https://github.com/ServiceNow/broad-openood | ServiceNow/broad-openood | 7a9414801544c1a4b759e453873442be9600fbe1 | repos/RxhOEngX8s.zip | fabf981968cbe5eac8c6fabe51b49999f2959f63732864d5ac83962bd7c00857 | 561,816 | 427 | {
".sh": 238,
".py": 186,
".ipynb": 3
} | 372 | {
"Python": 647435,
"Jupyter Notebook": 276787,
"Shell": 158806
} | false | 2023-08-22T11:04:29 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/expecting-the-unexpected-towards-broad-out-of"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} | |
luajgSjRlew | 2,023 | rejected | Social and environmental impact of recent developments in machine learning on biology and chemistry research | [
"Daniel Probst"
] | [
"~Daniel_Probst2"
] | OpenReview API | Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and... | Reject | null | 4 | [
{
"id": "tXuo3ALmsDI",
"reviewer_signature": [
"ICLR.cc/2023/Conference/Paper1682/Reviewer_dCqg"
],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=luajgSjRlew | 2210.00356 | papers/luajgSjRlew.pdf | dcc1ee623895a8498778699700f9e1b72419c3090dd29c22687b51dc1038b4bd | 423,657 | openreview | https://github.com/daenuprobst/anon_aichem | daenuprobst/anon_aichem | 1fed4455d480029851d7311b30808ddefc50654d | repos/luajgSjRlew.zip | 583381e7daaa78a14c6dbf0d3f50a698cfc14ee750e40908f7dc3520a62080d6 | 989,642 | 12 | {
".py": 8,
".ipynb": 4
} | 977 | {
"Jupyter Notebook": 1948436,
"Python": 42528
} | false | 2022-09-29T11:43:18 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/social-and-environmental-impact-of-recent"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
UTTrevGchy | 2,022 | rejected | Learning Diverse Options via InfoMax Termination Critic | [
"Yuji Kanagawa",
"Tomoyuki Kaneko"
] | [
"~Yuji_Kanagawa1",
"~Tomoyuki_Kaneko1"
] | OpenReview API | We consider the problem of autonomously learning reusable temporally extended actions, or options, in reinforcement learning. While options can speed up transfer learning by serving as reusable building blocks, learning reusable options for unknown task distribution remains challenging. Motivated by the recent success ... | Reject | null | 4 | [
{
"id": "X4FfHup_yfx",
"reviewer_signature": [
"ICLR.cc/2022/Conference/Paper1337/Reviewer_SWkr"
],
"rating": "",
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some piec... | https://openreview.net/forum?id=UTTrevGchy | 2010.02756 | papers/UTTrevGchy.pdf | fa886e840c90f0a4921bbced03dbf0cdd122b5d0f8b317d4ee38dcfd82fd0221 | 7,825,823 | openreview | https://github.com/kngwyu/infomax-option-critic | kngwyu/infomax-option-critic | 9d907c041c1d0280db9b23eb2fdf9e0033e33bf3 | repos/UTTrevGchy.zip | dd5ed1420d3d685e407d63b957414d00e8da56dba9d9e49fd7bbb6a9bd0f099d | 4,359,739 | 12 | {
".py": 12
} | 4,256 | {
"Python": 94250
} | false | 2020-10-07T04:19:32 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/diverse-exploration-via-infomax-options-1"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
Jf24xdaAwF9 | 2,021 | rejected | Self-Activating Neural Ensembles for Continual Reinforcement Learning | [
"Sam Powers",
"Abhinav Gupta"
] | [
"~Sam_Powers1",
"~Abhinav_Gupta1"
] | OpenReview API | The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries which simplify the problem considera... | Reject | null | 4 | [
{
"id": "MinW53KvF_e",
"reviewer_signature": [
"ICLR.cc/2021/Conference/Paper2033/AnonReviewer4"
],
"rating": "6: Marginally above acceptance threshold",
"confidence": "3: The reviewer is fairly confident that the evaluation is correct",
"recommendation": "",
"soundness": "",
"... | https://openreview.net/forum?id=Jf24xdaAwF9 | 2301.00141 | papers/Jf24xdaAwF9.pdf | 0eee8234ca66b80d4b4cbb8a116b278961d35b81e7d05e13adcd1baee7aac1d3 | 2,871,749 | openreview | https://github.com/AGI-Labs/continual_rl | AGI-Labs/continual_rl | f2754bb282757829765beb4703f24b87efa13ff9 | repos/Jf24xdaAwF9.zip | efbc4a642120339684e9f830a7a2108670d7a6ed80ab446c4a3718ba15a19373 | 677,532 | 88 | {
".py": 88
} | 2,633 | {
"Python": 480548
} | false | 2023-07-06T14:04:25 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/self-activating-neural-ensembles-for-1"
} | {
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"paper_code_mapping": "Papers With Code archive",
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} |
Hd1Wciuyka | 2,026 | rejected | Jodi: Unification of Visual Generation and Understanding via Joint Modeling | [
"Yifeng Xu",
"Zhenliang He",
"Meina Kan",
"Shiguang Shan",
"Xilin Chen"
] | [
"~Yifeng_Xu1",
"~Zhenliang_He2",
"~Meina_Kan1",
"~Shiguang_Shan2",
"~Xilin_Chen1"
] | OpenReview API | Visual generation and understanding are two deeply interconnected aspects of human intelligence, yet they have been traditionally treated as separate tasks in machine learning. In this paper, we propose Jodi, a diffusion framework that unifies visual generation and understanding by jointly modeling the image domain and... | Reject | 4 | [
{
"id": "yZsoqUSigf",
"reviewer_signature": [
"ICLR.cc/2026/Conference/Submission12035/Reviewer_yWSM"
],
"rating": 6,
"soundness": 4,
"presentation": 3,
"contribution": 4,
"confidence": 4,
"summary": "This paper introduces Jodi, a diffusion-based model that tries to unify i... | https://openreview.net/forum?id=Hd1Wciuyka | 2505.19084 | papers/Hd1Wciuyka.pdf | 7e113f1eb5e658bf8bbc3e34d4e2aaccd272d507edfd06b5760315be4b56ed16 | 33,196,323 | openreview | https://github.com/VIPL-GENUN/Jodi | VIPL-GENUN/Jodi | c21bdcb8817284980f68321fa7375c5376c37d6d | repos/Hd1Wciuyka.zip | 0ff0a1794f60f41eaadf7593950e0402bb0586fd822b1df416006c934d568acc | 8,646,852 | 72 | {
".py": 70,
".sh": 2
} | 8,435 | {
"Python": 500890,
"Shell": 870
} | false | 2026-03-06T02:46:10 | {
"method": "exact_normalized_title",
"pwc_official": true,
"pwc_mentioned_in_paper": true,
"pwc_url": "https://paperswithcode.com/paper/jodi-unification-of-visual-generation-and"
} | {
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"paper_code_mapping": "Papers With Code archive",
"repository_snapshot": "GitHub API commit-pinned ZIP"
} |
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