paper_id stringlengths 10 10 | year int64 2.02k 2.03k | conference stringclasses 3
values | accepted bool 2
classes | title stringlengths 14 183 | abstract stringlengths 246 3.6k | keywords listlengths 1 28 | pdf_url stringlengths 40 40 | paper_text stringlengths 53 566k ⌀ | reviews listlengths 2 8 |
|---|---|---|---|---|---|---|---|---|---|
009LK0vLcY | 2,023 | NeurIPS 2023 | true | Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation | The design and analysis of randomized experiments is fundamental to many areas, from the physical and social sciences to industrial settings.
Regression adjustment is a popular technique to reduce the variance of estimates obtained from experiments, by utilizing information contained in auxiliary covariates.
While th... | [
"regression adjustment; treatment effect estimation; average treatment effect"
] | https://openreview.net/pdf?id=009LK0vLcY | Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
Abstract
The design and analysis of randomized experiments is fundamental to many areas, from the physical and social sciences to industrial settings. Regression adjustment is a popular technique to reduce the varian... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this paper, authors present regression adjusted estimators for estimating the average treatment effect under the Bernoulli design.\\nIn particular, they show that by using the leverage scores and a ridge regression adjustment, favorable finite sample bounds... |
00EKYYu3fD | 2,023 | NeurIPS 2023 | true | Complexity Matters: Rethinking the Latent Space for Generative Modeling | In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the pr... | [
"generative model",
"latent space",
"distance between distributions",
"generative adversarial network",
"vqgan"
] | https://openreview.net/pdf?id=00EKYYu3fD | Complexity Matters: Rethinking the Latent Space for Generative Modeling
Abstract
In generative modeling, numerous successful approaches leverage a lowdimensional latent space, e.g., Stable Diffusion [68] models the latent space induced by an encoder and generates images through a paired decoder. Although the selectio... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work investigates what constitutes a good latent space for generative models, and proposes a new training paradigm for generative models – DAE. Simply put, with DAE generative models are trained as Autoencoder in two stages. First, a relatively weak decod... |
01GQK1gwe3 | 2,023 | NeurIPS 2023 | false | Can Neural Networks Improve Classical Optimization of Inverse Problems? | Finding the values of model parameters from data is an essential task in science.
While iterative optimization algorithms like BFGS can find solutions to inverse problems with machine precision for simple problems, their reliance on local information limits their effectiveness for complex problems involving local minim... | [
"Inverse problems",
"neural networks",
"iterative optimization",
"chaos",
"convergence"
] | https://openreview.net/pdf?id=01GQK1gwe3 | 1
Abstract
Finding the values of model parameters from data is an essential task in science. While iterative optimization algorithms like BFGS can find solutions to inverse problems with machine precision for simple problems, their reliance on local information limits their effectiveness for complex problems involvin... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this paper the authors explore whether better optimization solutions can be found by jointly optimizing several inverse problems together. These inverse problems share a connection as they all can be formulated through a differentiable function $F(\\\\xi_i ... |
02Uc0G2Cym | 2,023 | NeurIPS 2023 | true | Robustness Guarantees for Adversarially Trained Neural Networks | We study robust adversarial training of two-layer neural networks as a bi-level optimization problem. In particular, for the inner loop that implements the adversarial attack during training using projected gradient descent (PGD), we propose maximizing a \emph{lower bound} on the $0/1$-loss by reflecting a surrogate lo... | [
"Adversarial training",
"neural networks",
"robustness",
"guarantees"
] | https://openreview.net/pdf?id=02Uc0G2Cym | Robustness Guarantees for Adversarially Trained Neural Networks
Abstract
We study robust adversarial training of two-layer neural networks as a bi-level optimization problem. In particular, for the inner loop that implements the adversarial attack during training using projected gradient descent (PGD), we propose max... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the optimization convergence of adversarial training in two-layer neural networks. This paper also proposes a reflecting loss which search for a better attack.\\n\\nSTRENGTHS:\\nThe paper is clear and easy to understand.\\n\\nWEAKNESSES:\\nM... |
05P1U0jk8r | 2,023 | NeurIPS 2023 | true | Exploiting hidden structures in non-convex games for convergence to Nash equilibrium | A wide array of modern machine learning applications – from adversarial models to multi-agent reinforcement learning – can be formulated as non-cooperative games whose Nash equilibria represent the system’s desired operational states. Despite having a highly non-convex loss landscape, many cases of interest possess a l... | [
"Nash Equilibrium",
"Games",
"Gradient",
"Non-monotone VI",
"Natural Gradient",
"Precondition"
] | https://openreview.net/pdf?id=05P1U0jk8r | Exploiting Hidden Structures in Non-Convex Games for Convergence to Nash Equilibrium
Abstract
Awide array of modern machine learning applications - from adversarial models to multi-agent reinforcement learning - can be formulated as non-cooperative games whose Nash equilibria represent the system's desired operationa... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposes a preconditioned hidden gradient descent to provide strong formal convergence guarantees in a general class\\nof multi-agent settings which are referred to as call hidden monotone games. Theoretical analyses and synthetic experiments are al... |
08hStXdT1s | 2,023 | NeurIPS 2023 | true | Knowledge Diffusion for Distillation | The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort to complicated training schemes, loss functions, and feature alignments, which are task-specific and feature-specific. In this paper, we sta... | [
"knowledge distillation",
"diffusion models"
] | https://openreview.net/pdf?id=08hStXdT1s | Knowledge Diffusion for Distillation
Abstract
The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort to complicated training schemes, loss functions, and feature alignments, which are task-spec... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis authors propose to explicitly eliminate the noises in student feature with a diffusion model to reduce the dicrepancy between student and teacher model for better knowledge distillation. Specifically, they build a lightweight diffusion model to reduce com... |
08zf7kTOoh | 2,023 | NeurIPS 2023 | true | Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models | We systematically study a wide variety of generative models spanning semantically-diverse image datasets to understand and improve the feature extractors and metrics used to evaluate them.
Using best practices in psychophysics, we measure human perception of image realism for generated samples by conducting the largest... | [
"generative models",
"generative model evaluation",
"self-supervised learning",
"representation learning",
"metrics"
] | https://openreview.net/pdf?id=08zf7kTOoh | Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Abstract
We systematically study a wide variety of generative models spanning semanticallydiverse image datasets to understand and improve the feature extractors and metrics used to evaluate them. Using best practices... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors performed extensive experimental study on various image-based generative models. Based on the study, it showed that no existing metric strongly correlated with human evaluations. The authors also included alternative self-supervised features extrac... |
090ORrOAPL | 2,023 | NeurIPS 2023 | true | On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection | Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their ... | [
"Out-of-distribution detection"
] | https://openreview.net/pdf?id=090ORrOAPL | On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Abstract
Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper notices that while outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers. The paper uncovers the benefits of using textual outlie... |
09bZyE9tfp | 2,023 | NeurIPS 2023 | true | Online Ad Procurement in Non-stationary Autobidding Worlds | Today's online advertisers procure digital ad impressions through interacting with autobidding platforms: advertisers convey high level procurement goals via setting levers such as budget, target return-on-investment, max cost per click, etc.. Then ads platforms subsequently procure impressions on advertisers' behalf, ... | [
"autobidding",
"online advertising",
"bandit online convex optimization",
"constrained optimization"
] | https://openreview.net/pdf?id=09bZyE9tfp | Online Ad Procurement in Non-stationary Autobidding Worlds
Abstract
Today's online advertisers procure digital ad impressions through interacting with autobidding platforms: advertisers convey high level procurement goals via setting levers such as budget, target return-on-investment, max cost per click, etc. Then ad... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work studies an advertiser's online high-dimensional lever decision problem with long-tern constraints under limited bandit feedback for different input models. The authors' main contributions include: (1) model formulation; (2) proposing an algorithm uni... |
0A9f2jZDGW | 2,023 | NeurIPS 2023 | true | Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models | Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be improved on these tasks, while negating them leads to task forgetting. Yet, our understanding of the eff... | [
"model editing",
"transfer learning",
"neural tangent kernel",
"vision-language pre-training",
"deep learning science"
] | https://openreview.net/pdf?id=0A9f2jZDGW | Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Abstract
Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be improved on thes... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the \\\"task vectors\\\" framework where the weights of models can be perturbed in specified directions corresponding to tasks which result in improvements on those tasks. They attribute the success of this framework to \\\"weight disentangl... |
0BfQT652sC | 2,023 | NeurIPS 2023 | true | Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk | We consider the stochastic multi-armed bandit problem and fully characterize the interplays among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. We show how the order of expected regret exactly affects the decaying rate of the regret tail probab... | [
"multi-armed bandit",
"worst-case optimality",
"instance-dependent consistency",
"light-tailed risk"
] | https://openreview.net/pdf?id=0BfQT652sC | Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk
Abstract
We consider the stochastic multi-armed bandit problem and fully characterize the interplays among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper tackles the problem of trading off problem-dependent and worst-case regret and \\\"tail risk\\\" of the regret in bandits. Here, the tail risk means the probability that the regret is larger than $\\\\Omega(T^\\\\delta)$ for some $\\\\delta>0$. Rece... |
0BwB03qA5T | 2,023 | NeurIPS 2023 | true | Gaussian Process Probes (GPP) for Uncertainty-Aware Probing | Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented... | [
"Interpretability",
"probing",
"Bayesian",
"Gaussian process",
"transparency"
] | https://openreview.net/pdf?id=0BwB03qA5T | Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Abstract
Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple fra... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper provides Gaussian process probes (GGP), a probabilistic method to evaluate uncertainty for a binary classification task over a (pre-trained) feature extractor. The core idea is to use GP instead of a linear probe on the feature extractor. The use of... |
0CbmvZPBGB | 2,023 | NeurIPS 2023 | true | Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage | In partial label learning (PLL), each sample is associated with a group of candidate labels, among which only one label is correct. The key of PLL is to disambiguate the candidate label set to find the ground-truth label. To this end, we first construct a constrained regression model to capture the confidence of the ca... | [
"partial label learning",
"dissimilarity propagation",
"candidate label shrinkage"
] | https://openreview.net/pdf?id=0CbmvZPBGB | Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
Abstract
In partial label learning (PLL), each sample is associated with a group of candidate labels, among which only one label is correct. The key of PLL is to disambiguate the candidate label set to find the ground-truth label. ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this submission, authors propose a novel partial label learning method named DPCLS by realizing the effectiveness of the dissimilarity relationship. They develop a semantic similarity and dissimilarity matrix which form an adversarial relationship, which is... |
0DpKUzl1Se | 2,023 | NeurIPS 2023 | true | Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations | Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent features. Existing approaches tend to focus on uncertainty on discrete cl... | [
"Bayesian deep learning",
"high-dimensional testing",
"uncertainty estimation",
"out-of-distribution detection"
] | https://openreview.net/pdf?id=0DpKUzl1Se | Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Abstract
Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on lowdimensional distributional assumptions and thus suffer from the high d... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper presents a new framework for uncertainty estimation in baysian neural networks. The core contribution is formulating uncertainty estimation as multiple high-dimensional hypothesis testing problem and deriving the test statistics necessary. The paper ... |
0EG6qUQ4xE | 2,023 | NeurIPS 2023 | true | AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation | Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently.
However, natural language exhibits a far more pronounced sequential dependency ... | [
"text generation",
"diffusion model",
"auto-regression",
"sequential dependency"
] | https://openreview.net/pdf?id=0EG6qUQ4xE | AR-DIFFUSION: Auto-Regressive Diffusion Model for Text Generation
Abstract
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. How... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper presents a diffusion model on text generation. The idea is generally interesting. It learns to diffuse sentence-level and token-level diffusion, where the latter one is diffused with dynamic movement speeds.Its experiments are well-designed and its ... |
0FhKURbTyF | 2,023 | NeurIPS 2023 | true | Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric | Reward shaping is an effective technique for integrating domain knowledge into reinforcement learning (RL). However, traditional approaches like potential-based reward shaping totally rely on manually designing shaping reward functions, which significantly restricts exploration efficiency and introduces human cognitive... | [
"Reinforcement learning",
"reward shaping",
"potential-based exploration",
"inverse dynamic bisimulation metric"
] | https://openreview.net/pdf?id=0FhKURbTyF | Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
Abstract
Reward shaping is an effective technique for integrating domain knowledge into reinforcement learning (RL). However, traditional approaches like potential-based reward shaping totally rely on manually de... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper introduces a novel approach that combines bisimulation metrics with inverse dynamics modeling to formulate potential functions for reward shaping. The integration of these techniques offers potential-based exploration, and the paper provides theoret... |
0Iw2dLh8uq | 2,023 | NeurIPS 2023 | true | Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity | Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in isolation, while in practice the environment is often evolving, leaving many related tasks to be solved. In this paper, we investigate the benefits of meta-learning in solving multiple MARL tasks collectively. We establish the f... | [
"Reinforcement learning",
"game theory",
"multi-agent systems",
"meta-learning"
] | https://openreview.net/pdf?id=0Iw2dLh8uq | Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
Abstract
Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in isolation, while in practice the environment is often evolving, leaving many related tasks to be solved. In this paper, we invest... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the interdependence between the convergence of MARL and the quality of policy initialization.\\n\\nSTRENGTHS:\\n1. It proposes a new algorithm that has an initialization-dependent convergence guarantee.\\n2. It establishes several theoretica... |
0K1ZTfHZ0N | 2,023 | NeurIPS 2023 | true | Dynamic Non-monotone Submodular Maximization | Maximizing submodular functions has been increasingly used in many applications of machine learning, such as data summarization, recommendation systems, and feature selection. Moreover, there has been a growing interest in both submodular maximization and dynamic algorithms.
In 2020, Monemizadeh and
Lattanzi, Mitrovi... | [
"Non-monotone submodular maximization",
"dynamic algorithm",
"oracle query",
"video summarization"
] | https://openreview.net/pdf?id=0K1ZTfHZ0N | Dynamic Non-monotone Submodular Maximization
Abstract
Maximizing submodular functions has been increasingly used in many applications of machine learning, such as data summarization, recommendation systems, and feature selection. Moreover, there has been a growing interest in both submodular maximization and dynamic ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work studies non-monotone submodular maximization subject to a cardinality\\nconstraint in a fully dynamic setting, i.e., maintaining a good solution as\\nelements are inserted and deleted from the \\\"current\\\" ground set. Studying\\nnon-monotone submo... |
0LmWBhIYLi | 2,023 | NeurIPS 2023 | true | Universal Prompt Tuning for Graph Neural Networks | In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appropriate prompt-based tuning methods for graph neural networks.... | [
"graph neural networks",
"prompt tuning"
] | https://openreview.net/pdf?id=0LmWBhIYLi | Universal Prompt Tuning for Graph Neural Networks
Abstract
In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field exhibits diverse pre-training strategies, posing challenges in designing appro... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper introduces a universal prompt-based tuning method called Graph Prompt Feature (GPF) and its variation (GPF-plus) for pre-trained Graph Neural Network (GNN) models. GPF is a universal method that can be applied to any pre-trained GNN model under any p... |
0N73P8pH2l | 2,023 | NeurIPS 2023 | true | ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection | In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and alleviate vanishing gradient. Unfortunately, UNet often suffers from unstable training in diffusion models which can be alleviated by scaling i... | [
"Diffusion Model",
"Stable Training",
"Network architectures"
] | https://openreview.net/pdf?id=0N73P8pH2l | ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
Abstract
In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and alleviate vanishing gradient. Un... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThey state that diffusion models using Unet suffer from unstable training\\nand oscillations of features and gradients. They also state that while this is sensitive to coefficients related to scaling the skip connections of Unet. They set out to provide an exp... |
0NuseeBuB4 | 2,023 | NeurIPS 2023 | true | Gaussian Mixture Solvers for Diffusion Models | Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differential equations (ODEs). In comparison, SDE-based solvers can generate s... | [
"Diffusion models",
"SDE-based solver",
"Gaussian mixture",
"Stroke-based synthesis"
] | https://openreview.net/pdf?id=0NuseeBuB4 | Gaussian Mixture Solvers for Diffusion Models
Abstract
Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differential equation... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors point out that $q(x_s|x_t)$ is not necessarily Gaussian when $t$ is significantly bigger than $s$, and propose to use a mixture of Gaussians in order to model better the reverse process, when the number of integration steps is not large. Such a sel... |
0OImBCFsdf | 2,023 | NeurIPS 2023 | true | SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning | Geometric representation learning of molecules is challenging yet essential for applications in multiple domains. Despite the impressive breakthroughs made by geometric deep learning in various molecular representation learning tasks, effectively capturing complicated geometric features across spatial dimensions is sti... | [
"geometric deep learning",
"molecule property prediction",
"geometric representation learning"
] | https://openreview.net/pdf?id=0OImBCFsdf | SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
Abstract
Geometric representation learning of molecules is challenging yet essential for applications in multiple domains. Despite the impressive breakthroughs made by geometric deep learning in various molecular representation learning... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper introduces a novel molecule representation network that enhances the learning capacity and scalability through the integration of innovative initialization techniques and activation functions for vector features. The conducted experiments validate t... |
0ORqsMY6OL | 2,023 | NeurIPS 2023 | true | Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains a critical bottleneck, particularly for natural policy gradient (NPG) methods, which are second-order. To address this issue, we propose th... | [
"reinforcement learning",
"federated learning"
] | https://openreview.net/pdf?id=0ORqsMY6OL | Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Abstract
Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains a critical bottleneck, pa... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposes a communication-efficient algorithm, FedNPG-ADMM, for federated natural policy gradient by using a reformulation of quadratic problem. It reduces the communication complexity from $\\\\mathcal{O}(d^2)$ to $\\\\mathcal{O}(d)$. The convergenc... |
0OU1ZXXxs5 | 2,023 | NeurIPS 2023 | true | Pruning vs Quantization: Which is Better? | Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date, only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question of which is better: neural network quantization or pruning? By answering this question, we h... | [
"Neural network quantization",
"neural network pruning",
"magnitude pruning",
"post-training quantization",
"quantization-aware training"
] | https://openreview.net/pdf?id=0OU1ZXXxs5 | Pruning vs Quantization: Which is Better?
Abstract
Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question on which is better: neural network quant... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis submission conducts a series empiricial experiments and anlysis between neural network pruning and quantization. It first used some statistics method to compare pruning/quantization. Then it measure the per-layer error based on a post-training compression... |
0P6uJtndWu | 2,023 | NeurIPS 2023 | true | Efficient Diffusion Policies For Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametri... | [
"Offline Reinforcement Learning",
"Diffusion Models"
] | https://openreview.net/pdf?id=0P6uJtndWu | Efficient Diffusion Policies for Offline Reinforcement Learning
Abstract
Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL [37] significantly boosts the performance of offline RL by r... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper focuses on the improvement of computation efficiency of Diffusion-QL by adopting the property of marginal distribution in the diffusion model and the variance control scheme proposed by DPM-Solver. Besides, this paper extends the scope of compatibil... |
0QpwcDPiHT | 2,023 | NeurIPS 2023 | false | Language Models Implement Simple Word2Vec-style Vector Arithmetic | A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a computational mechanism familiar from traditional word embeddings: the use of simple vector arithmetic in order to encode abstract relations (e.g., Pol... | [
"interpretability",
"nlp",
"neural networks",
"deep learning",
"explainability",
"representation learning"
] | https://openreview.net/pdf?id=0QpwcDPiHT | Abstract
A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a computational mechanism familiar from traditional word embeddings: the use of simple vector arithmetic in order to encode abstract relations ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper presents evidence that LMs sometimes use а computational mechanism similar to traditional word embeddings, specifically using simple vector arithmetic to encode abstract relations. Experiments show that this mechanism is specific to tasks that requir... |
0Tq1RGJBid | 2,023 | NeurIPS 2023 | true | A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging | This work is concerned with the estimation problem of linear model when the
sample size is extremely large and the data dimension can vary with the sample
size. In this setting, the least square estimator based on the full data is not feasible
with limited computational resources. Many existing methods for this problem... | [
"Big data",
"Data averaging",
"Order statistic",
"Sampling method",
"Sketching method."
] | https://openreview.net/pdf?id=0Tq1RGJBid | A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
Abstract
This work is concerned with the estimation problem of linear model when the sample size is extremely large and the data dimension can vary with the sample size. In this setting, the least square estimator based on the full data is ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the linear regression problem and proposes a new sketching method based on data averaging.\\n\\nSTRENGTHS:\\nPlease see the \\\"questions\\\" section.\\n\\nWEAKNESSES:\\nPlease see the \\\"questions\\\" section.\\n\\nQUESTIONS:\\nThis topic ... |
0VcvYQ3uPh | 2,023 | NeurIPS 2023 | true | Improved Frequency Estimation Algorithms with and without Predictions | Estimating frequencies of elements appearing in a data stream is a key task in large-scale data analysis. Popular sketching approaches to this problem (e.g., CountMin and CountSketch) come with worst-case guarantees that probabilistically bound the error of the estimated frequencies for any possible input. The work of ... | [
"learning-augmented algorithms",
"algorithms with predictions",
"data-driven algorithms",
"sublinear",
"streaming",
"frequency estimation",
"sketching"
] | https://openreview.net/pdf?id=0VcvYQ3uPh | Improved Frequency Estimation Algorithms with and without Predictions
Abstract
Estimating frequencies of elements appearing in a data stream is a key task in largescale data analysis. Popular sketching approaches to this problem (e.g., CountMin and CountSketch) come with worst-case guarantees that probabilistically b... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studied frequency estimation and learning-augmented frequency estimation. CountMin and CountSketch are the most popular algorithms for this task. With the addition of learning augmentation, an algorithm is given access to a learned prediction, in th... |
0WLMVDdvDF | 2,023 | NeurIPS 2023 | true | No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions | Existing online learning algorithms for adversarial Markov Decision Processes
achieve $\mathcal{O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed.
This is because it has been shown that a... | [
"reinforcement Learning",
"best of both worlds",
"MDP",
"robust RL",
"adversarial corruption"
] | https://openreview.net/pdf?id=0WLMVDdvDF | No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Abstract
Existing online learning algorithms for adversarial Markov Decision Processes achieve O ( √ T ) regret after T rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the tra... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper introduces the challenge of online learning in adversarial MDPs where the loss functions and transition functions are chosen by a malicious adversary. Although previous algorithms achieving $O(\\\\sqrt{T})$ regret with fixed transition functions cou... |
0Wp3VHX0Gm | 2,023 | NeurIPS 2023 | true | Score-based Generative Models with Lévy Processes | Investigating the optimal stochastic process beyond Gaussian for noise injection in a score-based generative model remains an open question. Brownian motion is a light-tailed process with continuous paths, which leads to a slow convergence rate for the Number of Function Evaluation (NFE). Recent studies have shown that... | [
"Generative Model",
"Score-based Method",
"Lévy processes"
] | https://openreview.net/pdf?id=0Wp3VHX0Gm | Score-based Generative Models with Lévy Processes
Abstract
Investigating the optimal stochastic process beyond Gaussian for noise injection in a score-based generative model remains an open question. Brownian motion is a light-tailed process with continuous paths, which leads to a slow convergence rate for the Number... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nScore-based generative models (SBGMs) generally employ Brownian motion, also known as the Wiener process, for noise injection. However, using Brownian motion in SBGMs often leads to issues such as mode collapse or slow sampling. To address these problems, the ... |
0e4eiXoUn5 | 2,023 | NeurIPS 2023 | true | SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise | The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary components accurately using multi... | [
"Time series",
"System Identification",
"Singular Spectrum Analysis"
] | https://openreview.net/pdf?id=0e4eiXoUn5 | SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
Abstract
The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors propose SAMoSSA, an algorithm that combines deterministic trend estimation via mSSA with estimation of an autoregressive component of a time series. They provide error rates for trend estimation, estimation of the AR coefficients, as well as the p... |
0eRDQQK2TW | 2,023 | NeurIPS 2023 | true | A Finite-Particle Convergence Rate for Stein Variational Gradient Descent | We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target distribution is sub-Gaussian with a Lipschitz score, SVGD with $n$ particles and an appr... | [
"Stein Variational Gradient Descent",
"SVGD",
"variational inference",
"sampling",
"optimization",
"Stein's method"
] | https://openreview.net/pdf?id=0eRDQQK2TW | A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
Abstract
We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target dis... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper provides an analysis of the convergence rate of finite-sample Stein Variational Gradient Descent (SVGD) for sub-Gaussian targets with Lipschitz scores. In contrast to previous works such as Liu 2017, Duncan et al. 2019 and Korba et al. 2020, the pre... |
0eXniewIvr | 2,023 | NeurIPS 2023 | true | Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing | Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds depend critically on the underlying geometry. While graph neural networks have been successfu... | [
"message passing",
"dynamics",
"mesh",
"symmetry",
"equivariance"
] | https://openreview.net/pdf?id=0eXniewIvr | Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
Abstract
Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds dep... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the problem of gauge equivariant convolutional and attentional architectures on meshes and proposes to introduce non-linear activations to enhance the model. The experiments on three models shows the performance of the proposed method.\\n\\n... |
0gvtoxhvMY | 2,023 | NeurIPS 2023 | true | Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition | This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data imbalance to model va... | [
"Imbalanced Node Classification",
"Bias-Variance Decomposition",
"Graph Neural Networks"
] | https://openreview.net/pdf?id=0gvtoxhvMY | Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Abstract
This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Varian... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper studies imbalanced node classification problem and propose a novel perspective to understand graph imbalance via bias-variance decomposition.By leveraging graph data augmentation, the paper develops a regularization technique to approximate the mode... |
0hwq2vOHT4 | 2,023 | NeurIPS 2023 | true | Described Object Detection: Liberating Object Detection with Flexible Expressions | Detecting objects based on language information is a popular task that includes Open-Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this paper, we advance them to a more practical setting called *Described Object Detection* (DOD) by expanding category names to flexible language expre... | [
"open-vocabulary object detection",
"referring expression comprehension",
"multi-modal detection"
] | https://openreview.net/pdf?id=0hwq2vOHT4 | Described Object Detection: Liberating Object Detection with Flexible Expressions
Abstract
Detecting objects based on language information is a popular task that includes Open-Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this paper, we advance them to a more practical setting cal... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper presents a new multi-modal computer vision task, called Described Object Detection, which is a superset of existing OVD and REC tasks. In particular, the DOD task seeks to create models which can detect multiple instances of something in images, from... |
0jZH883i34 | 2,023 | NeurIPS 2023 | true | Model Sparsity Can Simplify Machine Unlearning | In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have ... | [
"Machine unlearning",
"model pruning"
] | https://openreview.net/pdf?id=0jZH883i34 | Model Sparsity Can Simplify Machine Unlearning
Abstract
In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the r... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper considers the benefit of leveraging sparsity to improve standard unlearning techniques. They empirically verify that across a wide range of datasets and architectures. the sparsity benefits unlearning.\\n\\nSTRENGTHS:\\n1. The recap of this paper on... |
0kz5RmHxmE | 2,023 | NeurIPS 2023 | true | Graph Contrastive Learning with Stable and Scalable Spectral Encoding | Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral domain also plays a vital role in complementing spatial views. However, existing s... | [
"Graph Contrastive Learning",
"Spectral Embedding"
] | https://openreview.net/pdf?id=0kz5RmHxmE | Graph Contrastive Learning with Stable and Scalable Spectral Encoding
Abstract
Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral d... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper proposes a method for Graph Contrastive Learning (GCL) by contrasting the spatial and spectral views ($Sp^2GCL$). The spatial view is obtained using a message passing GNN. For the spectral view the authors propose an equivariant model called EigenMLP... |
0rEJx5QAxt | 2,023 | NeurIPS 2023 | true | Convex-Concave Zero-Sum Markov Stackelberg Games | Zero-sum Markov Stackelberg games can be used to model myriad problems, in domains ranging from economics to human robot interaction. In this paper, we develop policy gradient methods that solve these games in continuous state and action settings using noisy gradient estimates computed from observed trajectories of pla... | [
"Stackelberg games",
"Equilibrium Computation",
"Policy Gradient"
] | https://openreview.net/pdf?id=0rEJx5QAxt | Convex-Concave 0-Sum Markov Stackelberg Games
Abstract
Zero-sum Markov Stackelberg games can be used to model myriad problems, in domains ranging from economics to human robot interaction. We develop a policy gradient method which we prove solves these games in continuous state, continuous action settings, using nois... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper develops policy gradient methods using stochastic gradient estimates from the trajectories of play for computing in polynomial time Stackelberg equilibria in convex-concave games, most notably including a certain class of reach-avoid problems. The a... |
0rVXQEeFEL | 2,023 | NeurIPS 2023 | true | Transformer-based Planning for Symbolic Regression | Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR have demonstrated the effectiveness of pre-trained transformer models in generating equations as sequences, leveraging large-scale pre-training... | [
"Symbolic Regression",
"Transformers",
"Planning",
"Deep Learning"
] | https://openreview.net/pdf?id=0rVXQEeFEL | Transformer-based Planning for Symbolic Regression
Abstract
Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR have demonstrated the effectiveness of pre-trained transformer models in generati... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper introduces TPSR, a Transformer-based Planning strategy for Symbolic Regression. TPSR incorporates Monte Carlo Tree Search into the transformer decoding process, enabling the integration of non-differentiable feedback such as accuracy and complexity. ... |
0tEjORCGFD | 2,023 | NeurIPS 2023 | true | Collaborative Score Distillation for Consistent Visual Editing | Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video or 3D scene), achieving consistency across a se... | [
"Score Distillation Sampling",
"Diffusion model",
"Editing"
] | https://openreview.net/pdf?id=0tEjORCGFD | Collaborative Score Distillation for Consistent Visual Editing
Abstract
Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as mu... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper introduces an approach to achieve consistent visual editing by leveraging a pre-trained pix2pix diffusion model. The authors propose a generalization of the SDS loss (originating from DreamFusion) to a CSD loss, which utilizes Stein variational gradi... |
0tnhFpyWjb | 2,023 | NeurIPS 2023 | true | Two-Stage Predict+Optimize for MILPs with Unknown Parameters in Constraints | Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the ... | [
"Constraint optimization",
"Predict+Optimize"
] | https://openreview.net/pdf?id=0tnhFpyWjb | Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
Abstract
Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training su... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nPredict+Optimize is an emerging paradigm that lies in the intersection of classical optimization (particularly mixed integer programming) and machine learning. Specifically, it considers the setting where a parameterized optimization problem:\\n$$ x^{\\\\star}... |
0uARg5G04K | 2,023 | NeurIPS 2023 | true | The Adversarial Consistency of Surrogate Risks for Binary Classification | We study the consistency of surrogate risks for robust binary classification.
It is common to learn robust classifiers by adversarial training, which seeks to minimize the expected $0$-$1$ loss when each example can be maliciously corrupted within a small ball.
We give a simple and complete characterization of the se... | [
"Adversarial learning",
"surrogate risks",
"optimal transport"
] | https://openreview.net/pdf?id=0uARg5G04K | The Adversarial Consistency of Surrogate Risks for Binary Classification
Abstract
We study the consistency of surrogate risks for robust binary classification. It is common to learn robust classifiers by adversarial training, which seeks to minimize the expected 0 -1 loss when each example can be maliciously corrupte... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proves the necessary and sufficient condition for a loss function to be adversarially consistent.\\nIn the previous literature, either adversarial consistency for restricted hypothesis spaces or negative results for adversarial consistency has been ... |
0vdEHDwamk | 2,023 | NeurIPS 2023 | true | Lovász Principle for Unsupervised Graph Representation Learning | This paper focuses on graph-level representation learning that aims to represent graphs as vectors that can be directly utilized in downstream tasks such as graph classification.
We propose a novel graph-level representation learning principle called Lovász principle, which is motivated by the Lovász number in graph t... | [
"Lovász Number",
"graph-level representation learning",
"unsupervised learning",
"semi-supervised learning"
] | https://openreview.net/pdf?id=0vdEHDwamk | Lovász Principle for Unsupervised Graph Representation Learning
Abstract
This paper focuses on graph-level representation learning that aims to represent graphs as vectors that can be directly utilized in downstream tasks such as graph classification. We propose a novel graph-level representation learning principle c... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper introduces the \\\"Lovasz Principle\\\", an unsupervised or semi-supervised graph representation learning approach inspired by the Lovasz number. The motivation for using the Lovasz Principle is well established, and the authors extensively discuss r... |
0x2Ou3xHbH | 2,023 | NeurIPS 2023 | true | On the Convergence of No-Regret Learning Dynamics in Time-Varying Games | Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the convergence of no-regret learning algorithms in dynamic multiagent settings. In this paper, we characterize the convergence of optimistic gradient... | [
"no-regret learning",
"optimistic gradient descent",
"time-varying games",
"dynamic regret"
] | https://openreview.net/pdf?id=0x2Ou3xHbH | On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Abstract
Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the convergence of no-regret learning algorithms in dynamic multiag... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper considers the problem of online learning in time-varying games under different setups. Specifically, the authors consider the case where all the players apply optimistic gradient descent (OGD) algorithm with a certain choice of learning rate. The ma... |
0ycX03sMAT | 2,023 | NeurIPS 2023 | true | Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | Federated zeroth-order optimization (FedZO) algorithm enjoys the advantages of both zeroth-order optimization and federated learning, and has shown exceptional performance on black-box attack and softmax regression tasks. However, there is no generalization analysis for FedZO, and its analysis on computing convergence ... | [
"Federated zeroth-order optimization",
"stability analysis",
"theoretical guarantee",
"non-convex optimization",
"sub-Weibull distribution"
] | https://openreview.net/pdf?id=0ycX03sMAT | Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
Abstract
Federated zeroth-order optimization (FedZO) algorithm enjoys the advantages of both zeroth-order optimization and federated learning, and has shown exceptional performance on black-box attack and softmax regression tasks. However, there... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper provides generalization analysis of federated zeroth order optimization.\\n\\nSTRENGTHS:\\nThe paper is well-written and addresses a relevant problem. The theoretical results appear correct, although I haven't thoroughly checked them.\\n\\nWEAKNESSE... |
0zeLTZAqaJ | 2,023 | NeurIPS 2023 | true | Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction | Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the search space. To address this issue, we propose a novel probability tree state abstraction ... | [
"reinforcement learning",
"mento carlo tree search",
"state abstraction"
] | https://openreview.net/pdf?id=0zeLTZAqaJ | Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction
Abstract
Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the sea... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper presents a novel approach called Probability Tree State Abstraction (PTSA) to improve the efficiency of Monte Carlo Tree Search (MCTS) algorithms, which have shown remarkable performance in challenging tasks. The computational complexity of MCTS alg... |
14R8QBKzFH | 2,023 | NeurIPS 2023 | false | Tight Bounds for Machine Unlearning via Differential Privacy | We consider the formulation of "machine unlearning" of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called "right to be forgotten" by requiring that a trained model, upon request, should be able to 'unlearn' a number of points from the training data, as if they had never been included in... | [
"machine unlearning",
"differential privacy",
"privacy"
] | https://openreview.net/pdf?id=14R8QBKzFH | Abstract
We consider the formulation of "machine unlearning" of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called "right to be forgotten" by requiring that a trained model, upon request, should be able to 'unlearn' a number of points from the training data, as if they had never been i... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the machine unlearning problem from the perspective of differential privacy. Specifically, the authors propose to use differentially private models directly so that unlearning update is not necessary (or unlearning is an identity map), and t... |
14ZM7FfPx8 | 2,023 | NeurIPS 2023 | true | Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent | Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SVGD has remained a challenging problem. For sampling from a Gaussian target, the SVGD dynamics with a bilinear kernel will remain Gaussian as... | [
"Stein variational gradient descent",
"Gaussian variational inference",
"Rates of Convergence"
] | https://openreview.net/pdf?id=14ZM7FfPx8 | Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Abstract
Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based deterministic sampling algorithm. Despite its wide usage, understanding the theoretical properties of SVGD has remained a challenging problem. For samp... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper focuses on the theoretical understanding and algorithmic contributions of Gaussian-Stein Variational Gradient Descent (Gaussian-SVGD) for Gaussian Variational Inference (GVI). The paper discusses the dynamics of Gaussian-SVGD and provides convergenc... |
17Zkztjlgt | 2,023 | NeurIPS 2023 | true | Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment | Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG forecasting, but they often struggle with temporal out-of-distribution (OoD) issues and dynamic spatial causation. In this paper, we propos... | [
"Spatio-temporal forecasting"
] | https://openreview.net/pdf?id=17Zkztjlgt | Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Abstract
Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG forecasting, but they often struggle with temporal ou... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper introduces CaST, a novel framework designed to address challenges in Spatio-Temporal Graph (STG) forecasting. CaST tackles issues related to temporal out-of-distribution and dynamic spatial causation by leveraging a causal lens and employing techniqu... |
19AgWnmyoV | 2,023 | NeurIPS 2023 | true | Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives | Goal-conditioned reinforcement learning (RL) is a powerful approach for learning general-purpose skills by reaching diverse goals. However, it has limitations when it comes to task-conditioned policies, where goals are specified by temporally extended instructions written in the Linear Temporal Logic (LTL) formal langu... | [
"Goal-Conditioned Reinforcement Learning",
"Linear Temporal Logic"
] | https://openreview.net/pdf?id=19AgWnmyoV | Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives
Abstract
Goal-conditioned reinforcement learning (RL) is a powerful approach for learning general-purpose skills by reaching diverse goals. However, it has limitations when it comes to task-conditioned policies, where goals are ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper considers the problem of instructing goal-conditioned RL agents to follow specifications expressed in Linear Temporal Logic (LTL) formulae. The proposed method works as follows. First, construct a Buchi automaton from the LTL specification, which is... |
1A4ZqTmnye | 2,023 | NeurIPS 2023 | true | Task-aware Distributed Source Coding under Dynamic Bandwidth | Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based ... | [
"Data Compression",
"Distributed Source Coding",
"Semantic Communication",
"Multi-sensor Networks",
"Bandwidth Allocation",
"Information Theory"
] | https://openreview.net/pdf?id=1A4ZqTmnye | Task-aware Distributed Source Coding under Dynamic Bandwidth
Abstract
Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node. A decoder at the central node d... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposed a task-aware distributed source coding framework called NDPCA (Neural Distributed Principal Component Analysis). This framework aimed to solve the problem of efficient compression of correlated data in multi-sensor networks. In section 2 an... |
1B6YKnHYBb | 2,023 | NeurIPS 2023 | true | De novo Drug Design using Reinforcement Learning with Multiple GPT Agents | *De novo* drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforc... | [
"De novo drug design",
"Molecular generation",
"Multi-agent reinforcement learning",
"GPT"
] | https://openreview.net/pdf?id=1B6YKnHYBb | De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
Abstract
De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposes a method named MolRL-MGPT for drug molecular generation.\\nConcretely, GPT-based agents are used to iteratively generate candidate compounds, and a special reward signal is adpoted to encourage agents to explore in diverse directions.\\nThe... |
1CJ8D7P8RZ | 2,023 | NeurIPS 2023 | true | PoET: A generative model of protein families as sequences-of-sequences | Generative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on a large multiple sequence alignment (MSA) from the specific family of interest, making... | [
"protein fitness prediction",
"transformer",
"retrieval",
"language model",
"MSA",
"generative model",
"protein engineering"
] | https://openreview.net/pdf?id=1CJ8D7P8RZ | PoET: A generative model of protein families as sequences-of-sequences
Abstract
Generative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on a la... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposed an autoregressive generation pre-trained model of protein families. The models are trained over the sequences-of-sequences being organized by a set of specific protein sequences. It utilized a shared in-sequence position encoder to capture ... |
1CpVHL10fh | 2,023 | NeurIPS 2023 | true | Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations | Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity. In this paper, we study the early s... | [
"Randomized experiments",
"heterogeneous effects",
"causal machine learning",
"fairness",
"sequential testing",
"clinical trials",
"A/B testing"
] | https://openreview.net/pdf?id=1CpVHL10fh | Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Abstract
Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work proposes a method for adapting stopping tests of randomized experiments in heterogeneous populations. Specifically, the authors motivate the problem, namely why heterogeneous treatment effects lead to late stopping of randomized experiments, for inst... |
1DTCoyAFiV | 2,023 | NeurIPS 2023 | true | Cascading Contextual Assortment Bandits | We present a new combinatorial bandit model, the \textit{cascading contextual assortment bandit}. This model serves as a generalization of both existing cascading bandits and assortment bandits, broadening their applicability in practice. For this model, we propose our first UCB bandit algorithm, UCB-CCA. We prove that... | [
"cascade bandit",
"assortment bandit",
"upper confidence bound",
"exploration and exploitation",
"combinatorial optimization"
] | https://openreview.net/pdf?id=1DTCoyAFiV | Cascading Contextual Assortment Bandits
Abstract
We present a new combinatorial bandit model, the cascading contextual assortment bandit . This model serves as a generalization of both existing cascading bandits and assortment bandits, broadening their applicability in practice. For this model, we propose our first U... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the contextual cascading assortment bandit problem, and proposes low regret algorithms for this problem.\\n\\nSTRENGTHS:\\n1. This paper studies a novel problem by combining ideas from assortment bandits and cascading bandits. \\n\\t2. The p... |
1DmP6ySKYq | 2,023 | NeurIPS 2023 | true | HeadSculpt: Crafting 3D Head Avatars with Text | Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models.
However, existing methods still struggle to create high-fidelity 3D head avatars in two aspects:
(1) The... | [
"3D generative model",
"head avatar",
"diffusion models",
"neural rendering"
] | https://openreview.net/pdf?id=1DmP6ySKYq | HeadSculpt: Crafting 3D Head Avatars with Text
Abstract
Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models. However, existing methods still struggle to crea... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work proposes a text-to-avatar creation pipeline, building upon dreamfusion and magic3d. To alleviate the geometric ambiguity, the authors replace the vanilla stable diffusion with a landmark-conditioned diffusion model finetuned with controlnet. They als... |
1EYKYJeZtR | 2,023 | NeurIPS 2023 | true | Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows | Modern language models excel at integrating across long temporal scales needed to encode linguistic meaning and show non-trivial similarities to biological neural systems. Prior work suggests that human brain responses to language exhibit hierarchically organized "integration windows" that substantially constrain the o... | [
"language modeling",
"temporal integration",
"transformers",
"timescales",
"model interpretation"
] | https://openreview.net/pdf?id=1EYKYJeZtR | Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows
Abstract
Modern language models excel at integrating across long temporal scales needed to encode linguistic meaning and show non-trivial similarities to biological neural systems. Prior ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors investigate the temporal integration window of several transformer language models (but focus on gpt2) by evaluating the effect of word swaps on the activations of individual units as a function of their distance from the swapped word. They then ch... |
1FVmMlifl7 | 2,023 | NeurIPS 2023 | true | How a Student becomes a Teacher: learning and forgetting through Spectral methods | In theoretical Machine Learning, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. A student network is trained on data generated by a fixed teacher network until it matches the instructor’s ability to cope with the assigned task. The above scheme proves particularly releva... | [
"Network Slimming",
"Spectral Analysis",
"Node Pruning",
"Teacher-Student"
] | https://openreview.net/pdf?id=1FVmMlifl7 | How a student becomes a teacher: learning and forgetting through Spectral methods
Abstract
In theoretical Machine Learning, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. A student network is trained on data generated by a fixed teacher network until it matches the inst... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors propose a novel technique that allows identifying an invariant subnetwork in a student model that mirrors the characteristics of the teacher in terms of computing neurons, path distribution, and topological attributes.\\n\\nSTRENGTHS:\\n- The manus... |
1G7CBp8o7L | 2,023 | NeurIPS 2023 | true | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering | Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task ... | [
"complex query answering",
"neural link prediction",
"knowledge graph embeddings",
"knowledge graphs",
"relational learning",
"adapters"
] | https://openreview.net/pdf?id=1G7CBp8o7L | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Abstract
Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this probl... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper addresses the challenging task of answering complex queries on incomplete knowledge graphs, where missing knowledge introduces additional complexity. Previous approaches either employed end-to-end architectures with opaque reasoning processes or rel... |
1GxKVprbwM | 2,023 | NeurIPS 2023 | true | On Computing Pairwise Statistics with Local Differential Privacy | We study the problem of computing pairwise statistics, i.e., ones of the form $\binom{n}{2}^{-1} \sum_{i \ne j} f(x_i, x_j)$, where $x_i$ denotes the input to the $i$th user, with differential privacy (DP) in the local model. This formulation captures important metrics such as Kendall's $\tau$ coefficient, Area Under C... | [
"differential privacy",
"local differential privacy",
"pairwise statistics"
] | https://openreview.net/pdf?id=1GxKVprbwM | On Computing Pairwise Statistics with Local Differential Privacy
Abstract
We study the problem of computing pairwise statistics, i.e., ones of the form ( n 2 ) -1 ∑ i = j f ( x i , x j ) , where x i denotes the input to the i th user, with differential privacy (DP) in the local model. This formulation captures import... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this paper, the authors analyzed the problem of privately computing the quadratic form in the model of differential privacy, and provide non-interactive local DP algorithm with MSE upper/lower bounds with gap log(k). The paper further develops results for a... |
1HKJ3lPz6m | 2,023 | NeurIPS 2023 | true | Learning and Collusion in Multi-unit Auctions | In a carbon auction, licenses for CO2 emissions are allocated among multiple interested players. Inspired by this setting, we consider repeated multi-unit auctions with uniform pricing, which are widely used in practice. Our contribution is to analyze these auctions in both the offline and online settings, by designing... | [
"multi-unit auctions",
"repeated auctions",
"online learning",
"collusion",
"games and learning",
"lower bounds",
"multiplicative weight updates",
"bandit learning"
] | https://openreview.net/pdf?id=1HKJ3lPz6m | Learning and Collusion in Multi-unit Auctions ∗
Abstract
In a carbon auction, licenses for CO2 emissions are allocated among multiple interested players. Inspired by this setting, we consider repeated multi-unit auctions with uniform pricing, which are widely used in practice. Our contribution is to analyze these auc... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies a setting where a single seller runs repeated multi-unit auctions. In each multi-unit auction, there are $K$ identical units of good for sale. Each buyer individually has a valuation for the good with decreasing marginal returns and submits ... |
1IOU2329Za | 2,023 | NeurIPS 2023 | true | Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance | Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulated objects or multi-object scenes, effectively capturing inter-part transformations poses a challenge, as it becomes entangled with the overa... | [
"3D deep learning",
"equivariant network",
"pointcloud segmentation",
"multi-body system"
] | https://openreview.net/pdf?id=1IOU2329Za | Banana : Bana ch Fixed-Point N etwork for Pointcloud Segmentation with Inter-Part Equiv a riance
Abstract
Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulated objects or multi-object scenes... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper considers an important problem in learning on point clouds -- the equivariance under the SE(3) group. \\nNamely, the authors address the requirement of inter-part equivariance, essential for handling real-world scenarios, where an object can consist... |
1JlAV2paGu | 2,023 | NeurIPS 2023 | true | Blurred-Dilated Method for Adversarial Attacks | Deep neural networks (DNNs) are vulnerable to adversarial attacks, which lead to incorrect predictions. In black-box settings, transfer attacks can be conveniently used to generate adversarial examples. However, such examples tend to overfit the specific architecture and feature representations of the source model, res... | [
"Transferable adversarial example"
] | https://openreview.net/pdf?id=1JlAV2paGu | Blurred-Dilated Method for Adversarial Attacks
Abstract
Deep neural networks (DNNs) are vulnerable to adversarial attacks, which lead to incorrect predictions. In black-box settings, transfer attacks can be conveniently used to generate adversarial examples. However, such examples tend to overfit the specific archite... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors propose the Blurred-Dilated method (BD), which utilizes BlurPools and dilated convolutions on the source model when an adversarial attack is applied, to increase the transferability of the transfer-based attack. The method replaces the MaxPool laye... |
1M8nDkUU9b | 2,023 | NeurIPS 2023 | true | Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals | Graphs are ubiquitous in various domains, such as social networks and biological systems. Despite the great successes of graph neural networks (GNNs) in modeling and analyzing complex graph data, the inductive bias of locality assumption, which involves exchanging information only within neighboring connected nodes, re... | [
"graph neural networks (GNNs)",
"total variation (TV)",
"Euler–Lagrange equation",
"calculus of variations",
"over-smoothing",
"min-max optimization"
] | https://openreview.net/pdf?id=1M8nDkUU9b | Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Abstract
Graphs are ubiquitous in various domains, such as social networks and biological systems. Despite the great successes of graph neural networks (GNNs) in modeling and analyzing complex graph data, the inductive bi... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper focuses on devising a new inductive bias for cutting-edge graph application and present a general framework through the lens of variational analysis. To this end, the authors first introduce a new selective mechanism that can be easily integrated in... |
1MUxtSBUox | 2,023 | NeurIPS 2023 | true | Budgeting Counterfactual for Offline RL | The main challenge of offline reinforcement learning, where data is limited, arises from a sequence of counterfactual reasoning dilemmas within the realm of potential actions: What if we were to choose a different course of action? These circumstances frequently give rise to extrapolation errors, which tend to accumula... | [
"reinforcement learning",
"offline reinforcement learning",
"counterfactual reasoning"
] | https://openreview.net/pdf?id=1MUxtSBUox | Budgeting Counterfactual for Offline RL
Abstract
The main challenge of offline reinforcement learning, where data is limited, arises from a sequence of counterfactual reasoning dilemmas within the realm of potential actions: What if we were to choose a different course of action? These circumstances frequently give r... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposes a novel offline RL algorithm BCOL that builds on the idea of limiting the numbers of counterfactual decisions. Instead of enforcing policy or value regularization, BCOL follows the decisions of the behavioral policy in the majority of the s... |
1NY5i5fq5e | 2,023 | NeurIPS 2023 | false | Optical Transformers | The rapidly increasing size of deep-learning models has caused renewed and growing interest in alternatives to digital computers to dramatically reduce the energy cost of running state-of-the-art neural networks. Optical matrix-vector multipliers are best suited to performing computations with very large operands, whic... | [
"Optics",
"Transformers",
"Accelerator",
"Energy Efficiency",
"Power Consumption",
"LLM",
"Large Language Models",
"Hardware",
"Optical Neural Networks",
"Scaling",
"Scaling Laws",
"Quantization"
] | https://openreview.net/pdf?id=1NY5i5fq5e | Abstract
The rapidly increasing size of deep-learning models has caused renewed and growing interest in alternatives to digital computers to dramatically reduce the energy cost of running state-of-the-art neural networks. Optical matrix-vector multipliers are best suited to performing computations with very large oper... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors analyze the performance, efficiency, and robustness of free-space optical dot-product engines for Transformer accelerations. Measurement results on an SLM-based optical system are demonstrated on some layers in a GPT-like model. System performance/... |
1PnSOKQKvq | 2,023 | NeurIPS 2023 | true | Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data | Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between severa... | [
"partial information decomposition",
"estimation",
"bias",
"inter-area interaction",
"neuroscience"
] | https://openreview.net/pdf?id=1PnSOKQKvq | Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data
Abstract
Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for compu... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper proposed a new method for partial information decomposition (PID) on multivariate Gaussian distributions. The issue of bias was discussed, and a correction method was provided. The method was tested on synthetic canonical examples and real data.\\n\\... |
1SAzP7W43j | 2,023 | NeurIPS 2023 | true | Single-Stage Visual Query Localization in Egocentric Videos | Visual Query Localization on long-form egocentric videos requires spatio-temporal search and localization of visually specified objects and is vital to build episodic memory systems. Prior work develops complex multi-stage pipelines that leverage well-established object detection and tracking methods to perform VQL. Ho... | [
"Visual Query Localization",
"Egocentric Video",
"Spatial-Temporal Correspondence",
"Episodic Memory"
] | https://openreview.net/pdf?id=1SAzP7W43j | Single-Stage Visual Query Localization in Egocentric Videos
Abstract
Visual Query Localization on long-form egocentric videos requires spatio-temporal search and localization of visually specified objects and is vital to build episodic memory systems. Prior work develops complex multi-stage pipelines that leverage we... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper proposes VQLoC, an end-to-end trainable framework for Visual Query Localization (VQL) on long-form egocentric videos. Compared with Ego4D's multi-stage approaches, VQLoC proposes a single-stage process that efficiently localizes visually specified ob... |
1SF2tiopYJ | 2,023 | NeurIPS 2023 | true | CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion | Controllable scene synthesis aims to create interactive environments for numerous industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner. Existing methods, reliant on retrieval from extensive databases or pre-trained s... | [
"Scene Graph",
"Scene Synthesis",
"Diffusion Model",
"Graph Convolution Network"
] | https://openreview.net/pdf?id=1SF2tiopYJ | CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Abstract
Controllable scene synthesis aims to create interactive environments for numerous industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a comp... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper looks at the problem of learning a generative model for sampling 3D environments from a description based on a graph and natural language. The graph does correspond to objects that participate in the scene, and textual descriptions attached to nodes ... |
1TJaITmK2Q | 2,023 | NeurIPS 2023 | true | Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds | Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. For enhancing the accuracy of such machine learning methods, it is often effective to incorporate global topological features, which are typically extracted by ... | [
"point cloud",
"persistence homology",
"isometry-invariant networks",
"filtration learning"
] | https://openreview.net/pdf?id=1TJaITmK2Q | Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
Abstract
Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. For enhancing the accuracy of such machine learning method... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this manuscript, the authors develop a special module that can adaptively learn a suitable filtration function for persistent homology (PH) and its downstream tasks. In particular, their learning module is specially designed, so that resulting persistent ho... |
1WMdoiVMov | 2,023 | NeurIPS 2023 | true | Robust Knowledge Transfer in Tiered Reinforcement Learning | In this paper, we study the Tiered Reinforcement Learning setting, a parallel transfer learning framework, where the goal is to transfer knowledge from the low-tier (source) task to the high-tier (target) task to reduce the exploration risk of the latter while solving the two tasks in parallel. Unlike previous work, we... | [
"Reinforcement Learning Theory",
"Transfer RL",
"Tiered RL"
] | https://openreview.net/pdf?id=1WMdoiVMov | Robust Knowledge Transfer in Tiered RL
Abstract
In this paper, we study the Tiered Reinforcement Learning setting, a parallel transfer learning framework, where the goal is to transfer knowledge from the low-tier (source) task to the high-tier (target) task to reduce the exploration risk of the latter while solving t... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper presents an extension of \\\"Tiered-RL\\\", a multi-fidelity RL framework where a \\\"low-fidelity\\\" environment is executed in parallel with the \\\"high-fidelity\\\" environment, with the purpose of training faster while keeping near-optimal regr... |
1WpmOipyYI | 2,023 | NeurIPS 2023 | true | Tanh Works Better with Asymmetry | Batch Normalization is commonly located in front of activation functions, as proposed by the original paper. Swapping the order, i.e., using Batch Normalization after activation functions, has also been attempted, but its performance is generally not much different from the conventional order when ReLU or a similar act... | [
"Batch Normalization",
"Activation Functions",
"Saturation",
"Sparsity"
] | https://openreview.net/pdf?id=1WpmOipyYI | Tanh Works Better With Asymmetry
Abstract
Batch Normalization is commonly located in front of activation functions, as proposed by the original paper. Swapping the order, i.e., using Batch Normalization after activation functions, has also been attempted, but its performance is generally not much different from the c... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proves the hypothesis that asymmetric saturation benefits network performance by swapping the position of Batch Normalization and Tanh activation functions. The Swap model generates high sparsity and asymmetric saturation which enables Tanh to behav... |
1YEF6TA8Di | 2,023 | NeurIPS 2023 | true | Langevin Quasi-Monte Carlo | Langevin Monte Carlo (LMC) and its stochastic gradient versions are powerful algorithms for sampling from complex high-dimensional distributions. To sample from a distribution with density $\pi(\theta)\propto \exp(-U(\theta)) $, LMC iteratively generates the next sample by taking a step in the gradient direction $\nabl... | [
"Completely uniformly distributed; log-concave sampling; low-discrepancy; MCMC;"
] | https://openreview.net/pdf?id=1YEF6TA8Di | Langevin Quasi-Monte Carlo
Abstract
Langevin Monte Carlo (LMC) and its stochastic gradient versions are powerful algorithms for sampling from complex high-dimensional distributions. To sample from a distribution with density π ( θ ) ∝ exp( -U ( θ )) , LMC iteratively generates the next sample by taking a step in the ... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper analyzes the effect of using quasi-random numbers in place of the usual IID Gaussians for the driving noise of a Langevin algorithm. Assuming that the loss is strong convex and a the quasi-random numbers are completely uniformly distributed, a bound... |
1ZvEtnrHS1 | 2,023 | NeurIPS 2023 | true | Convolutional State Space Models for Long-Range Spatiotemporal Modeling | Effectively modeling long spatiotemporal sequences is challenging due to the need to model complex spatial correlations and long-range temporal dependencies simultaneously. ConvLSTMs attempt to address this by updating tensor-valued states with recurrent neural networks, but their sequential computation makes them slow... | [
"spatiotemporal modeling",
"ConvLSTM",
"RNN",
"state spaces",
"SSM",
"S4",
"S5",
"long-range dependencies",
"video prediction"
] | https://openreview.net/pdf?id=1ZvEtnrHS1 | Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Abstract
Effectively modeling long spatiotemporal sequences is challenging due to the need to model complex spatial correlations and long-range temporal dependencies simultaneously. ConvLSTMs attempt to address this by updating tensor-valued stat... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper presents ConvS5, a convolutional state-space model that aims to model long-range spatiotemporal dependencies in video data. They extend the prior S5 model to operate in a convolutional state-space, and retain the long-range benefits of state-space m... |
1ZzG6td0el | 2,023 | NeurIPS 2023 | true | Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints | We consider distributed parameter estimation using interactive protocols subject to local information constraints such as bandwidth limitations, local differential privacy, and restricted measurements. We provide a unified framework enabling us to derive a variety of (tight) minimax lower bounds for different parametri... | [
"statistical estimation; interactivity; local differential privacy; communication constraint"
] | https://openreview.net/pdf?id=1ZzG6td0el | Unified lower bounds for interactive high-dimensional estimation under information constraints
Abstract
We consider distributed parameter estimation using interactive protocols subject to local information constraints such as bandwidth limitations, local differential privacy, and restricted measurements. We provide a... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper discusses parametric estimation under a communication setup. This setup adds variation to classic parametric estimation and focuses on the setup $\\\\theta \\\\to X^n \\\\to Y^n$ where the goal is to estimate $\\\\theta$ given $Y^n$, which is genera... |
1aQivXgZKj | 2,023 | NeurIPS 2023 | true | Incentivized Communication for Federated Bandits | Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despite their compelling theoretical guarantee on performance and communication efficiency, this assumption is overly idealistic and oftentimes v... | [
"contextual bandit",
"federated learning",
"incentive mechanism"
] | https://openreview.net/pdf?id=1aQivXgZKj | Incentivized Communication for Federated Bandits
Abstract
Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despite their compelling theoretical guarantee on performance and communication effic... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors study a new problem in federated bandits that involves incentivizing clients to share data. They propose a solution called Inc-FedUCB, which offers incentives in a linear contextual bandit setting. They demonstrate that Inc-FedUCB can achieve near-... |
1bTG4sJ7tN | 2,023 | NeurIPS 2023 | true | A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes | The proximal policy optimization (PPO) algorithm stands as one of the most prosperous methods in the field of reinforcement learning (RL). Despite its success, the theoretical understanding of PPO remains deficient. Specifically, it is unclear whether PPO or its optimistic variants can effectively solve linear Markov d... | [
"policy optimization",
"adversarial lienar MDPs",
"RL theory"
] | https://openreview.net/pdf?id=1bTG4sJ7tN | A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
Abstract
The proximal policy optimization (PPO) algorithm stands as one of the most prosperous methods in the field of reinforcement learning (RL). Despite its success, the theoretical understanding of PPO remains de... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this paper, the authors extend the theory of proximal policy optimization-based methods in the linear mixture MDPs and propose an optimistic variant PPO algorithm (OPPO+) for stochastic linear MDPs and adversarial linear MDPs with full information. \\nThe p... |
1cY5WLTN0k | 2,023 | NeurIPS 2023 | false | Monte Carlo Neural PDE Solver | Training neural PDE solver in an unsupervised manner is essential in scenarios with limited available or high-quality data. However, the performance and efficiency of existing methods are limited by the properties of numerical algorithms integrated during the training stage (like FDM and PSM), which require careful spa... | [
"Neural PDE Solver",
"Feynman-Kac Formula",
"AI for PDE"
] | https://openreview.net/pdf?id=1cY5WLTN0k | Abstract
Training neural PDE solver in an unsupervised manner is essential in scenarios with limited available or high-quality data. However, the performance and efficiency of existing methods are limited by the properties of numerical algorithms integrated during the training stage (like FDM and PSM), which require c... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors propose an unsupervised neural technique for solving PDEs based on the classical correspondence between (parabolic) partial differential equations (PDE) and stochastic differential equations (SDE) as given by the Feynman-Kac formula. Specifically, ... |
1g0A9kE8Id | 2,023 | NeurIPS 2023 | true | Learning Unseen Modality Interaction | Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-complete assumption for multimodal learning and instead strive for generalization to unseen modality combinations during inference. We pose the ... | [
"Multimodal Learning"
] | https://openreview.net/pdf?id=1g0A9kE8Id | Learning Unseen Modality Interaction
Abstract
Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-complete assumption for multimodal learning and instead strive for generalization to unseen moda... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work studies the problem of learning interactions of unseen modality combinations. Specifically, all training data is modality-incomplete, and the model must learn to perform inference on modality-complete data. The paper claims to be the first to study i... |
1h2TAUEfc4 | 2,023 | NeurIPS 2023 | true | Exact Verification of ReLU Neural Control Barrier Functions | Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time. Recently, machine learning metho... | [
"Safety",
"Neural Barrier Function",
"Verification"
] | https://openreview.net/pdf?id=1h2TAUEfc4 | Exact Verification of ReLU Neural Control Barrier Functions
Abstract
Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper studies the exact conditions under which a learned CBF $b$ with ReLU activations yields the positive invariance property. Even though the set of inputs for which the CBF is non-differentiable has measure zero, the paper shows by example that safety ... |
1h7Uh9zUXc | 2,023 | NeurIPS 2023 | true | Reliable learning in challenging environments | The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic guarantees have only been considered in very specific settings. In this work, we consider the design and analysis of reliable learners in ch... | [
"Reliable machine learning",
"adversarial robustness",
"distribution shift",
"theory"
] | https://openreview.net/pdf?id=1h7Uh9zUXc | Reliable learning in challenging environments
Abstract
The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic guarantees have only been considered in very specific settings. In this work, we con... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper develops learning methods that provide theoretical guarantees for point-wise predictions in challenging scenarios at test-time. In particular, the methods presented address situations affected by adversarial attacks and distribution shifts. The paper... |
1h92PmnKov | 2,023 | NeurIPS 2023 | true | Momentum Provably Improves Error Feedback! | Due to the high communication overhead when training machine learning models in a distributed environment, modern algorithms invariably rely on lossy communication compression. However, when untreated, the errors caused by compression propagate, and can lead to severely unstable behavior, including exponential divergen... | [
"Heavy-ball momentum",
"Polyak momentum",
"Error feedback",
"Federated Learning",
"Distributed Optimization",
"Stochastic optimization",
"Nonconvex optimization"
] | https://openreview.net/pdf?id=1h92PmnKov | Momentum Provably Improves Error Feedback!
Abstract
Due to the high communication overhead when training machine learning models in a distributed environment, modern algorithms invariably rely on lossy communication compression. However, when untreated, the errors caused by compression propagate, and can lead to seve... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper introduces a modification to the EF21-SGD algorithm by incorporating momentum, resulting in a new algorithm named EF21-SGDM. The innovative analysis accompanying this new method successfully addresses the challenges associated with EF21-SGD, reducin... |
1hZwxBgQ3G | 2,023 | NeurIPS 2023 | true | Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation | The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. To successfully accomplish the VON task, two essential conditions must be fulfiled: 1) the user knows the name of the desired object; and 2) the user-specified object actually is present within th... | [
"Visual Navigation",
"Demand-Driven Navigation"
] | https://openreview.net/pdf?id=1hZwxBgQ3G | Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Abstract
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. To successfully accomplish the VON task, two essential conditions must be fulfiled: 1)... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposes Demand-driven Navigation (DDN), which leverages the user’s demand as the task instruction and prompts the agent to find an object which matches the specified demand. This paper also proposes a method of first acquiring textual attribute fea... |
1ihGy9vAIg | 2,023 | NeurIPS 2023 | true | Towards Efficient Image Compression Without Autoregressive Models | Recently, learned image compression (LIC) has garnered increasing interest with its rapidly improving performance surpassing conventional codecs. A key ingredient of LIC is a hyperprior-based entropy model, where the underlying joint probability of the latent image features is modeled as a product of Gaussian distribut... | [
"Image Compression",
"Correlation"
] | https://openreview.net/pdf?id=1ihGy9vAIg | Towards Efficient Image Compression Without Autoregressive Models
Abstract
Recently, learned image compression (LIC) has garnered increasing interest with its rapidly improving performance surpassing conventional codecs. A key ingredient of LIC is a hyperprior-based entropy model, where the underlying joint probabili... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper aims at providing a efficient and effective entropy model to achieve better trade-off between performance and complexity for learned image compression. They introduce a correlation loss to force the latent to be spatially decorrelated so that it can... |
1jhmWkZGy6 | 2,023 | NeurIPS 2023 | true | Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis | Natural target functions and tasks typically exhibit hierarchical modularity -- they can be broken down into simpler sub-functions that are organized in a hierarchy. Such sub-functions have two important features: they have a distinct set of inputs (input-separability) and they are reused as inputs higher in the hierar... | [
"Neural networks",
"Hierarchical modularity",
"Pruning",
"Sparsity"
] | https://openreview.net/pdf?id=1jhmWkZGy6 | Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
Abstract
Natural target functions and tasks typically exhibit hierarchical modularity - they can be broken down into simpler sub-functions that are organized in a hierarchy. Such sub-functions ha... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nWhen conventionally trained, neural networks do not demonstrate structural properties like input separable functions or reusability of sub-modules. The authors investigated this phenomenon and proposed iterative pruning to enhance structural properties of neur... |
1kZx7JiuA2 | 2,023 | NeurIPS 2023 | true | Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics | Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereas convergen... | [
"AI4Science",
"Molecular Dynamics",
"equivariant neural networks",
"stochastic dynamics"
] | https://openreview.net/pdf?id=1kZx7JiuA2 | Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Abstract
Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. Howeve... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors propose Implicit Transfer Operator (ITO) learning, which aims to learn a surrogate of a molecular dynamics (MD) simulation process. Since standard MD simulations integrate Newton's equations of motion numerically, small integration time-steps are n... |
1kgK0r8PGg | 2,023 | NeurIPS 2023 | true | Exponentially Convergent Algorithms for Supervised Matrix Factorization | Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. Our goal is to use SMF to learn low-rank latent factors that offer interpretable, data-reconstructive, and class-disc... | [
"Supervised matrix factorization",
"multi-objective optimization",
"global convergence",
"linear convergence",
"statistical estimation"
] | https://openreview.net/pdf?id=1kgK0r8PGg | Exponentially Convergent Algorithms for Supervised Matrix Factorization
Abstract
Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. Our goal is to use SMF to learn lo... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper proposed a novel supervised dictionary model, with two variations, one feature-based and one filter-based. The problem setting is on classification tasks with both high- and low-dimensional feautures, where the high dimensional features are learnd t... |
1mAYtdoYw6 | 2,023 | NeurIPS 2023 | true | Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding | Spectral embedding is a powerful graph embedding technique that has received a lot of attention recently due to its effectiveness on Graph Transformers. However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graph... | [
"Graph Neural Networks",
"Positional Encoding",
"Spectral Embedding",
"Laplacian Eigenvectors"
] | https://openreview.net/pdf?id=1mAYtdoYw6 | Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Abstract
Spectral embedding is a powerful graph embedding technique that has received a lot of attention recently due to its effectiveness on Graph Transformers. However, from a theoretical perspective, the universal expressi... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors address the problem of expressiveness in graph neural networks. Positional encodings using spectral approaches have suffered from sign and basis invariance. The authors propose Laplacian canonization which finds unique representations, and they ana... |
1mJQq6zYaE | 2,023 | NeurIPS 2023 | true | Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations | The diffusion model has shown remarkable success in computer vision, but it remains unclear whether the ODE-based probability flow or the SDE-based diffusion model is more superior and under what circumstances. Comparing the two is challenging due to dependencies on data distributions, score training, and other numeric... | [
"diffusion models; stochastic differential equations; score-based generative models; asymptotic analysis"
] | https://openreview.net/pdf?id=1mJQq6zYaE | Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
Abstract
The diffusion model has shown remarkable success in computer vision, but it remains unclear whether the ODE-based probability flow or the SDE-based diffusion model is more superior and und... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe paper studies diffusion models, which comprise a class of generative models based on stochastic differential equations. In diffusion models, data is first transformed into Gaussian noise via a stochastic differential equation (usually an Ornstein-Uhlenbeck... |
1mdTYi1jAW | 2,023 | NeurIPS 2023 | true | Adjustable Robust Reinforcement Learning for Online 3D Bin Packing | Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints. While current deep reinforcement learning (DRL) methods for online 3D-BPP have shown promising results ... | [
"online 3D bin packing problem",
"combinatorial optimization problem",
"reinforcement learning"
] | https://openreview.net/pdf?id=1mdTYi1jAW | Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
Abstract
Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints. While current deep reinfor... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work proposed a novel adjustable and robust Reinforcement Learning framework for Online 3D Bin Packing task. The proposed method can achieve balance between performance and the worst-case environment.\\n\\nSTRENGTHS:\\nThe writing is clear, and the experi... |
1moStpWGUj | 2,023 | NeurIPS 2023 | true | Energy Guided Diffusion for Generating Neurally Exciting Images | In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems.
However, as we move up the visual hierarchy, the complexity of neuronal computations increases.
Conseq... | [
"most exciting inputs",
"diffusion models",
"energy guidance",
"attention",
"macaque V4"
] | https://openreview.net/pdf?id=1moStpWGUj | Energy Guided Diffusion for Generating Neurally Exciting Images
Abstract
In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hi... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this work, the authors first employed attention readout to train a model for predicting neural responses ($y$) from image ($x$), aiming to address the issue of attention effects in the V4 area. Subsequently, they applied the image ($x$) from text ($y$) meth... |
1osmdAfD4P | 2,023 | NeurIPS 2023 | true | Online Convex Optimization with Unbounded Memory | Online convex optimization (OCO) is a widely used framework in online
learning. In each round, the learner chooses a decision in a convex set and an
adversary chooses a convex loss function, and then the learner suffers the
loss associated with their current decision. However, in many applications the
learner's... | [
"online learning",
"online convex optimization",
"online linear control"
] | https://openreview.net/pdf?id=1osmdAfD4P | Online Convex Optimization with Unbounded Memory
Abstract
Online convex optimization (OCO) is a widely used framework in online learning. In each round, the learner chooses a decision in a convex set and an adversary chooses a convex loss function, and then the learner suffers the loss associated with their current d... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper focuses on online convex optimization with memory, a topic with increasing attention recently. Traditional framework assumes that the current environment is only affected by the decisions of a limited past, while this work considers that the current... |
1p6teT6F73 | 2,023 | NeurIPS 2023 | true | Alternating Updates for Efficient Transformers | It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a mo... | [
"efficiency",
"efficient transformers"
] | https://openreview.net/pdf?id=1p6teT6F73 | Alternating Updates for Efficient Transformers
Abstract
It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Upda... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis work proposes an efficient way to increase the width of transformer models, i.e. Alternating Updates (AltUp). AltUp can increase the width of one existing model with little computation overhead. Authors evaluated their approach on T5 model and see some im... |
1pWNhmbllE | 2,023 | NeurIPS 2023 | true | Uncertainty-Aware Instance Reweighting for Off-Policy Learning | Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various important real-world applications, such as search engines and recommender systems. While the ground-truth logging policy is usually unknown, previous work simply takes its est... | [
"off-policy learning",
"uncertainty"
] | https://openreview.net/pdf?id=1pWNhmbllE | Uncertainty-Aware Instance Reweighting for Off-Policy Learning
Abstract
Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as search engines and recommender systems. While the ground-truth loggi... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper delves into the issue of off-policy learning, the objective of which is to devise a new action selection policy based solely on the logged feedback derived from a logging policy. The paper pays particular attention to scenarios in which the logging ... |
1q0feiJ2i4 | 2,023 | NeurIPS 2023 | true | Large Language Models are Visual Reasoning Coordinators | Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsense reasoning ability in various domains. However, how to harness the collective power of these complementary VLMs is rarely explored. Existi... | [
"visual reasoning",
"large language models"
] | https://openreview.net/pdf?id=1q0feiJ2i4 | Large Language Models are Visual Reasoning Coordinators
Abstract
Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsense reasoning ability in various domains. However, how to harness the coll... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\n1. The author proposes to utilize a language model as the coordinator between different outputs from different VLMs, leveraging their strengths for visual reasoning.\\n2. The proposed method achieves SOTA results on multiple visual reasoning benchmarks. \\n3. ... |
1qFnxhdbxg | 2,023 | NeurIPS 2023 | true | Energy Discrepancies: A Score-Independent Loss for Energy-Based Models | Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo... | [
"Energy-based models",
"statistical discrepancy",
"latent-variable model",
"density estimation"
] | https://openreview.net/pdf?id=1qFnxhdbxg | Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Abstract
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) whic... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nEnergy discrepancy is presented as a new loss for the training of EBM. \\nED interpolates between the losses of score matching and maximum-likelihood estimation.\\nEfficacy of ED on a latent variable energy-based model is demonstrated to tackle manifold hypoth... |
1qvx610Cu7 | 2,023 | NeurIPS 2023 | true | Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation | Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with curated synthesis problems and test-cases, are used to measure the performance of various LLMs on code synthesis. However, these test-cases ca... | [
"LLM4Code",
"ChatGPT",
"Automated Test Generation"
] | https://openreview.net/pdf?id=1qvx610Cu7 | Is Your Code Generated by ChatGPT Really Correct?
Abstract
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with curated synthesis problems and test-cases, are used to measure the performance of... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThis paper leveraged large language model (LLM) based and mutation-based strategies to generate high-quality test cases for the popular dataset HumanEval. The extended dataset HumanEval+ provides a better code generation benchmark for assessing the performance... |
1recIOnzOF | 2,023 | NeurIPS 2023 | true | Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild | This paper presents Decorate3D, a versatile and user-friendly method for the creation and editing of 3D objects using images. Decorate3D models a real-world object of interest by neural radiance field (NeRF) and decomposes the NeRF representation into an explicit mesh representation, a view-dependent texture, and a dif... | [
"Texture Generation",
"Text-Driven",
"3D-Consistent Editing",
"Neural Radiance Field"
] | https://openreview.net/pdf?id=1recIOnzOF | Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild
Abstract
This paper presents Decorate3D, a versatile and user-friendly method for the creation and editing of 3D objects using images. Decorate3D models a real-world object of interest by neural radiance field (NeRF) and decompose... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors present Decorate3D, a technique for text-driven texturing of a 3D mesh given a NeRF representation of a given scene. To this end, the authors introduce a two-stage texturing scheme. First, the NeRF is decomposed into a 3D mesh and view-dependent te... |
1tviRBNxI9 | 2,023 | NeurIPS 2023 | true | Topological Obstructions and How to Avoid Them | Incorporating geometric inductive biases into models can aid interpretability and generalization, but encoding to a specific geometric structure can be challenging due to the imposed topological constraints. In this paper, we theoretically and empirically characterize obstructions to training encoders with geometric la... | [
"representation learning",
"variational autoencoders",
"homeomorphism",
"topological",
"equivariant",
"lie groups",
"normalizing flows"
] | https://openreview.net/pdf?id=1tviRBNxI9 | Topological Obstructions and How to Avoid Them
Abstract
Incorporating geometric inductive biases into models can aid interpretability and generalization, but encoding to a specific geometric structure can be challenging due to the imposed topological constraints. In this paper, we theoretically and empirically charac... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nThe authors investigate two types of topological obstructions that pose challenges for models aimed at learning a particularly structure in the embedding space. Specifically, the authors identify figure eight local minima and mismatches in winding numbers are ... |
1uirUsR9E7 | 2,023 | NeurIPS 2023 | true | Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture | Convolutional neural networks (CNNs) have recently emerged as promising models of the ventral visual stream, despite their lack of biological specificity.
While current state-of-the-art models of the primary visual cortex (V1) have surfaced from training with adversarial examples and extensively augmented data, these m... | [
"NeuroAI",
"Neuroscience",
"Visual Stream",
"Convolutional Neural Networks",
"Biologically inspired deep learning"
] | https://openreview.net/pdf?id=1uirUsR9E7 | Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
Abstract
Convolutional neural networks (CNNs) have recently emerged as promising models of the ventral visual stream, despite their lack of biological specificity. While current state-of-the-art models of the primary visual cortex (V1... | [
"{\"IS_META_REVIEW\": false, \"comments\": \"SUMMARY:\\nIn this study the authors propose the incorporation of mechanistic biologically inspired filtering and normalization components in deep convolutional networks (DCNs) with the goal of increased alignment of model responses to V1 neural responses and tuning prop... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.