text:
or enhancing model complexity. However, after analyzing DNN susceptibility to adversarial samples, we discover that the current defense mechanisms are limited and, more importantly, cannot provide theoretical guarantees of robustness against adversarial sampled-based attacks. As such, we propose a new adversary resistant technique that obstructs attackers from constructing impactful adversarial samples by randomly nullifying features within data vectors. Our proposed technique is evaluated on a real world dataset with 14,679 malware variants and 17,399 benign programs. We theoretically validate the robustness of our technique, and empirically show that our technique significantly boosts DNN robustness to adversarial samples while maintaining high accuracy in classification. To demonstrate the general applicability of our proposed method, we also conduct experiments using the MNIST and CIFAR-10 datasets, widely used in image recognition research.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1145–1153},
numpages = {9},
keywords = {deep neural networks, image recognition, adversarial sample, malware classification},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098164,
author = {Wang, Qi and Sun, Mengying and Zhan, Liang and Thompson, Paul and Ji, Shuiwang and Zhou, Jiayu},
title = {Multi-Modality Disease Modeling via Collective Deep Matrix Factorization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098164},
doi = {10.1145/3097983.3098164},
abstract = {Alzheimer's disease (AD), one of the most common causes of dementia, is a severe irreversible neurodegenerative disease that results in loss of mental functions. The transitional stage between the expected cognitive decline of normal aging and AD, mild cognitive impairment (MCI), has been widely regarded as a suitable time for possible therapeutic intervention. The challenging task of MCI detection is therefore of great clinical importance, where the key is to effectively fuse predictive information from multiple heterogeneous data sources collected from the patients. In this paper, we propose a framework to fuse multiple data modalities for predictive modeling using deep matrix factorization, which explores the non-linear interactions among the modalities and exploits such interactions to transfer knowledge and enable high performance prediction. Specifically, the proposed collective deep matrix factorization decomposes all modalities simultaneously to capture non-linear structures of the modalities in a supervised manner, and learns a modality specific component for each modality and a modality invariant component across all modalities. The modality invariant component serves as a compact feature representation of patients that has high predictive power. The modality specific components provide an effective means to explore imaging genetics, yielding insights into how imaging and genotype interact with each other non-linearly in the AD pathology. Extensive empirical studies using various data modalities provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method for fusing heterogeneous modalities.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1155–1164},
numpages = {10},
keywords = {medical imaging, multi-modality, matrix factorization, genetics, deep neural network, mild cognitive impairment},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098110,
author = {Wu, Tianyi and Sugawara, Shinya and Yamanishi, Kenji},
title = {Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098110},
doi = {10.1145/3097983.3098110},
abstract = {We propose a new model selection criterion based on the minimum description length principle in a name of the decomposed normalized maximum likelihood criterion. Our criterion can be applied to a large class of hierarchical latent variable models, such as the Naive Bayes models, stochastic block models and latent Dirichlet allocations, for which many conventional information criteria cannot be straightforwardly applied due to irregularity of latent variable models. Our method also has an advantage that it can be exactly evaluated without asymptotic approximation with small time complexity. Our experiments using synthetic and real data demonstrated validity of our method in terms of computational efficiency and model selection accuracy, while our criterion especially dominated the other criteria when sample size is small and when data are noisy.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1165–1174},
numpages = {10},
keywords = {clustering, topic and latent variable models, model selection},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098124,
author = {Wu, Fei and Anchuri, Pranay and Li, Zhenhui},
title = {Structural Event Detection from Log Messages},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098124},
doi = {10.1145/3097983.3098124},
abstract = {A wide range of modern web applications are only possible because of the composable nature of the web services they are built upon. It is, therefore, often critical to ensure proper functioning of these web services. As often, the server-side of web services is not directly accessible, several log message based analysis have been developed to monitor the status of web services. Existing techniques focus on using clusters of messages (log patterns) to detect important system events. We argue that meaningful system events are often representable by groups of cohesive log messages and the relationships among these groups. We propose a novel method to mine structural events as directed workflow graphs (where nodes represent log patterns, and edges represent relations among patterns). The structural events are inclusive and correspond to interpretable episodes in the system. The problem is non-trivial due to the nature of log data: (i) Individual log messages contain limited information, and (ii) Log messages in a large scale web system are often interleaved even though the log messages from individual components are ordered. As a result, the patterns and relationships mined directly from the messages and their ordering can be erroneous and unreliable in practice. Our solution is based on the observation that meaningful log patterns and relations often form workflow structures that are connected. Our method directly models the overall quality of structural events. Through both qualitative and quantitative experiments on real world datasets, we demonstrate the effectiveness and the expressiveness of our event detection method.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1175–1184},
numpages = {10},
keywords = {log summarization, structural event detection},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098127,
author = {Wu, Tao and Gleich, David F.},
title = {Retrospective Higher-Order Markov Processes for User Trails},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098127},
doi = {10.1145/3097983.3098127},
abstract = {Users form information trails as they checkin with a geolocation, rate items, or consume media. A common problem is to predict what a user might do next for the purposes of guidance, recommendation, or prefetching. Markov chains models have been widely used methods to study such sequences of data. First-order Markov chains are easy to estimate, but lack accuracy when history matters. Higher-order Markov chains, in contrast, have too many parameters and suffer from overfitting the training data. Fitting these parameters with regularization and smoothing only offers mild improvements. In this paper we propose the retrospective higher-order Markov process (RHOMP) as a low-parameter model for such sequences. This model is a special case of a higher-order Markov chain where the transitions depend retrospectively on a single history state instead of an arbitrary combination of history states. There are two immediate computational advantages: the number of parameters is linear in the order of the Markov chain and the model can be fit to large state spaces. Furthermore, by providing a specific structure to the higher-order chain, RHOMPs improve the model accuracy by efficiently utilizing history states without risks of overfitting the data. We demonstrate how to estimate a RHOMP from data and we demonstrate the effectiveness of our method on various real application datasets spanning geolocation data, review sequences, and business locations. The RHOMP model uniformly outperforms higher-order Markov chains, Kneser-Ney regularization, and tensor factorizations in terms of prediction accuracy.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1185–1194},
numpages = {10},
keywords = {user models, tensor factorization, higher-order markov chains},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098152,
author = {Xie, Liyang and Baytas, Inci M. and Lin, Kaixiang and Zhou, Jiayu},
title = {Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098152},
doi = {10.1145/3097983.3098152},
abstract = {Many data mining applications involve a set of related learning tasks. Multi-task learning (MTL) is a learning paradigm that improves generalization performance by transferring knowledge among those tasks. MTL has attracted so much attention in the community, and various algorithms have been successfully developed. Recently, distributed MTL has also been studied for related tasks whose data is distributed across different geographical regions. One prominent challenge of the distributed MTL frameworks is to maintain the privacy of the data. The distributed data may contain sensitive and private information such as patients' records and registers of a company. In such cases, distributed MTL frameworks are required to preserve the privacy of the data. In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge. A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations. The proposed asynchronous approach is robust against network delays and provides a guaranteed differential privacy through carefully designed perturbation. Theoretical guarantees of the proposed algorithm are derived and supported by the extensive experimental results.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1195–1204},
numpages = {10},
keywords = {asynchronous proximal optimization, differential privacy, multi-task learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098151,
author = {Xing, Zhengming and Hillygus, Sunshine and Carin, Lawrence},
title = {Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098151},
doi = {10.1145/3097983.3098151},
abstract = {Extensive information on 3 million randomly sampled United States citizens is used to construct a statistical model of constituent preferences for each U.S. congressional district. This model is linked to the legislative voting record of the legislator from each district, yielding an integrated model for constituency data, legislative roll-call votes, and the text of the legislation. The model is used to examine the extent to which legislators' voting records are aligned with constituent preferences, and the implications of that alignment (or lack thereof) on subsequent election outcomes. The analysis is based on a Bayesian formalism, with fast inference via a stochastic variational Bayesian analysis.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1205–1214},
numpages = {10},
keywords = {multiplicative gamma process, matrix factorization, topic model, ideal point, hierarchical dirichlet process, stochastic variational inference},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098103,
author = {Yamada, Makoto and Lian, Wenzhao and Goyal, Amit and Chen, Jianhui and Wimalawarne, Kishan and Khan, Suleiman A. and Kaski, Samuel and Mamitsuka, Hiroshi and Chang, Yi},
title = {Convex Factorization Machine for Toxicogenomics Prediction},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098103},
doi = {10.1145/3097983.3098103},
abstract = {We introduce the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Specifically, we employ a linear+quadratic model and regularize the linear term with the ℓ2-regularizer and the quadratic term with the trace norm regularizer. Then, we formulate the CFM optimization as a semidefinite programming problem and propose an efficient optimization procedure with Hazan's algorithm. A key advantage of CFM over existing FMs is that it can find a globally optimal solution, while FMs may get a poor locally optimal solution since the objective function of FMs is non-convex. In addition, the proposed algorithm is simple yet effective and can be implemented easily. Finally, CFM is a general factorization method and can also be used for other factorization problems, including multi-view matrix factorization and tensor completion problems, in various domains including toxicogenomics and bioinformatics. Through synthetic and traditionally used movielens datasets, we first show that the proposed CFM achieves results competitive to FMs. We then show in a toxicogenomics prediction task that CFM predicts the toxic outcomes of a collection of drugs better than a state-of-the-art tensor factorization method.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1215–1224},
numpages = {10},
keywords = {toxicogenomics prediction, factorization machines, convex},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098179,
author = {Yan, Yizhou and Cao, Lei and Kulhman, Caitlin and Rundensteiner, Elke},
title = {Distributed Local Outlier Detection in Big Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098179},
doi = {10.1145/3097983.3098179},
abstract = {In this work, we present the first distributed solution for the Local Outlier Factor (LOF) method -- a popular outlier detection technique shown to be very effective for datasets with skewed distributions. As datasets increase radically in size, highly scalable LOF algorithms leveraging modern distributed infrastructures are required. This poses significant challenges due to the complexity of the LOF definition, and a lack of access to the entire dataset at any individual compute machine. Our solution features a distributed LOF pipeline framework, called DLOF. Each stage of the LOF computation is conducted in a fully distributed fashion by leveraging our invariant observation for intermediate value management. Furthermore, we propose a data assignment strategy which ensures that each machine is self-sufficient in all stages of the LOF pipeline, while minimizing the number of data replicas. Based on the convergence property derived from analyzing this strategy in the context of real world datasets, we introduce a number of data-driven optimization strategies. These strategies not only minimize the computation costs within each stage, but also eliminate unnecessary communication costs by aggressively pushing the LOF computation into the early stages of the DLOF pipeline. Our comprehensive experimental study using both real and synthetic datasets confirms the efficiency and scalability of our approach to terabyte level data.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1225–1234},
numpages = {10},
keywords = {big data, distributed processing, local outlier},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098191,
author = {Yan, Yizhou and Cao, Lei and Rundensteiner, Elke A.},
title = {Scalable Top-n Local Outlier Detection},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098191},
doi = {10.1145/3097983.3098191},
abstract = {Local Outlier Factor (LOF) method that labels all points with their respective LOF scores to indicate their status is known to be very effective for identifying outliers in datasets with a skewed distribution. Since outliers by definition are the absolute minority in a dataset, the concept of Top-N local outlier was proposed to discover the n points with the largest LOF scores. The detection of the Top-N local outliers is prohibitively expensive, since it requires huge number of high complexity k-nearest neighbor (kNN) searches. In this work, we present the first scalable Top-N local outlier detection approach called TOLF. The key innovation of TOLF is a multi-granularity pruning strategy that quickly prunes most points from the set of potential outlier candidates without computing their exact LOF scores or even without conducting any kNN search for them. Our customized density-aware indexing structure not only effectively supports the pruning strategy, but also accelerates the $k$NN search. Our extensive experimental evaluation on OpenStreetMap, SDSS, and TIGER datasets demonstrates the effectiveness of TOLF - up to 35 times faster than the state-of-the-art methods.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1235–1244},
numpages = {10},
keywords = {local outlier factor, top-n, pruning strategy},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098094,
author = {Yang, Carl and Bai, Lanxiao and Zhang, Chao and Yuan, Quan and Han, Jiawei},
title = {Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098094},
doi = {10.1145/3097983.3098094},
abstract = {Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1245–1254},
numpages = {10},
keywords = {semi-supervised learning, recommender systems, neural networks, collaborative filtering},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098133,
author = {Yang, Pei and Tan, Qi and He, Jingrui},
title = {Multi-Task Function-on-Function Regression with Co-Grouping Structured Sparsity},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098133},
doi = {10.1145/3097983.3098133},
abstract = {The growing importance of functional data has fueled the rapid development of functional data analysis, which treats the infinite-dimensional data as continuous functions rather than discrete, finite-dimensional vectors. On the other hand, heterogeneity is an intrinsic property of functional data due to the variety of sources to collect the data. In this paper, we propose a novel multi-task function-on-function regression approach to model both the functionality and heterogeneity of data. The basic idea is to simultaneously model the relatedness among tasks and correlations among basis functions by using the co-grouping structured sparsity to encourage similar tasks to behave similarly in shrinking the basis functions. The resulting optimization problem is challenging due to the non-smoothness and non-separability of the co-grouping structured sparsity. We present an efficient algorithm to solve the problem, and prove its separability, convexity, and global convergence. The proposed algorithm is applicable to a wide spectrum of structured sparsity regularized techniques, such as structured ℓ2,p norm and structured Schatten p-norm. The effectiveness of the proposed approach is verified on benchmark functional data sets collected from various domains.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1255–1264},
numpages = {10},
keywords = {multi-task learning, co-grouping structured sparsity, generalized schatten norm, function-on-function regression},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098142,
author = {Ye, Wei and Zhou, Linfei and Mautz, Dominik and Plant, Claudia and B\"{o}hm, Christian},
title = {Learning from Labeled and Unlabeled Vertices in Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098142},
doi = {10.1145/3097983.3098142},
abstract = {Networks such as social networks, citation networks, protein-protein interaction networks, etc., are prevalent in real world. However, only very few vertices have labels compared to large amounts of unlabeled vertices. For example, in social networks, not every user provides his/her profile information such as the personal interests which are relevant for targeted advertising. Can we leverage the limited user information and friendship network wisely to infer the labels of unlabeled users?In this paper, we propose a semi-supervised learning framework called weighted-vote Geometric Neighbor classifier (wvGN) to infer the likely labels of unlabeled vertices in sparsely labeled networks. wvGN exploits random walks to explore not only local but also global neighborhood information of a vertex. Then the label of the vertex is determined by the accumulated local and global neighborhood information. Specifically, wvGN optimizes a proposed objective function by a search strategy which is based on the gradient and coordinate descent methods. The search strategy iteratively conducts a coarse search and a fine search to escape from local optima. Extensive experiments on various synthetic and real-world data verify the effectiveness of wvGN compared to state-of-the-art approaches.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1265–1274},
numpages = {10},
keywords = {regularization, semi-supervised learning, random walk, support vector machines, geometric neighborhood},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098147,
author = {Yin, Peifeng and Luo, Ping and Nakamura, Taiga},
title = {Small Batch or Large Batch? Gaussian Walk with Rebound Can Teach},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098147},
doi = {10.1145/3097983.3098147},
abstract = {Efficiency of large-scale learning is a hot topic in both academic and industry. The stochastic gradient descent (SGD) algorithm, and its extension mini-batch SGD, allow the model to be updated without scanning the whole data set. However, the use of approximate gradient leads to the uncertainty issue, slowing down the decreasing of objective function. Furthermore, such uncertainty may result in a high frequency of meaningless update on the model, causing a communication issue in parallel learning environment. In this work, we develop a batch-adaptive stochastic gradient descent (BA-SGD) algorithm, which can dynamically choose a proper batch size as learning proceeds. Particularly on the basis of Taylor extension and central limit theorem, it models the decrease of objective value as a Gaussian random walk game with rebound. In this game, a heuristic strategy of determining batch size is adopted to maximize the utility of each incremental sampling. By evaluation on multiple real data sets, we demonstrate that by smartly choosing the batch size, the BA-SGD not only conserves the fast convergence of SGD algorithm but also avoids too frequent model updates.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1275–1284},
numpages = {10},
keywords = {gaussian random walk, batch-adaptive sgd, rebound},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098135,
author = {You, Shan and Xu, Chang and Xu, Chao and Tao, Dacheng},
title = {Learning from Multiple Teacher Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098135},
doi = {10.1145/3097983.3098135},
abstract = {Training thin deep networks following the student-teacher learning paradigm has received intensive attention because of its excellent performance. However, to the best of our knowledge, most existing work mainly considers one single teacher network. In practice, a student may access multiple teachers, and multiple teacher networks together provide comprehensive guidance that is beneficial for training the student network. In this paper, we present a method to train a thin deep network by incorporating multiple teacher networks not only in output layer by averaging the softened outputs (dark knowledge) from different networks, but also in the intermediate layers by imposing a constraint about the dissimilarity among examples. We suggest that the relative dissimilarity between intermediate representations of different examples serves as a more flexible and appropriate guidance from teacher networks. Then triplets are utilized to encourage the consistence of these relative dissimilarity relationships between the student network and teacher networks. Moreover, we leverage a voting strategy to unify multiple relative dissimilarity information provided by multiple teacher networks, which realizes their incorporation in the intermediate layers. Extensive experimental results demonstrated that our method is capable of generating a well-performed student network, with the classification accuracy comparable or even superior to all teacher networks, yet having much fewer parameters and being much faster in running.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1285–1294},
numpages = {10},
keywords = {multiple teacher networks, knowledge transfer, triplet loss, deep learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098189,
author = {Yu, Linyun and Cui, Peng and Song, Chaoming and Zhang, Tianyang and Yang, Shiqiang},
title = {A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098189},
doi = {10.1145/3097983.3098189},
abstract = {Social behavior dynamics is one of the central building blocks in understanding and modeling complex social dynamic phenomena, such as information spreading, opinion formation, and social mobilization. While a wide range of models for social behavior dynamics have been proposed in recent years, the essential ingredients and the minimum model for social behavior dynamics is still largely unanswered. Here, we find that human interaction behavior dynamics exhibit rich complexities over the response time dimension and natural time dimension by exploring a large scale social communication dataset. To tackle this challenge, we develop a temporal Heterogeneous Survival framework where the regularities in response time dimension and natural time dimension can be organically integrated. We apply our model in two online social communication datasets. Our model can successfully regenerate the interaction patterns in the social communication datasets, and the results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines. Meanwhile, the learnt parameters and discovered statistical regularities can lead to multiple potential applications.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1295–1304},
numpages = {10},
keywords = {information diffusion modeling, human behaviors analysis, social dynamics},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098141,
author = {Zhan, Wang and Zhang, Min-Ling},
title = {Inductive Semi-Supervised Multi-Label Learning with Co-Training},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098141},
doi = {10.1145/3097983.3098141},
abstract = {In multi-label learning, each training example is associated with multiple class labels and the task is to learn a mapping from the feature space to the power set of label space. It is generally demanding and time-consuming to obtain labels for training examples, especially for multi-label learning task where a number of class labels need to be annotated for the instance. To circumvent this difficulty, semi-supervised multi-label learning aims to exploit the readily-available unlabeled data to help build multi-label predictive model. Nonetheless, most semi-supervised solutions to multi-label learning work under transductive setting, which only focus on making predictions on existing unlabeled data and cannot generalize to unseen instances. In this paper, a novel approach named COINS is proposed to learning from labeled and unlabeled data by adapting the well-known co-training strategy which naturally works under inductive setting. In each co-training round, a dichotomy over the feature space is learned by maximizing the diversity between the two classifiers induced on either dichotomized feature subset. After that, pairwise ranking predictions on unlabeled data are communicated between either classifier for model refinement. Extensive experiments on a number of benchmark data sets show that COINS performs favorably against state-of-the-art multi-label learning approaches.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1305–1314},
numpages = {10},
keywords = {inductive setting, co-training, semi-supervised learning, multi-label learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098109,
author = {Zhang, Yutao and Chen, Robert and Tang, Jie and Stewart, Walter F. and Sun, Jimeng},
title = {LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098109},
doi = {10.1145/3097983.3098109},
abstract = {Managing patients with complex multimorbidity has long been recognized as a difficult problem due to complex disease and medication dependencies and the potential risk of adverse drug interactions. Existing work either uses complicated rule-based protocols which are hard to implement and maintain, or simple statistical models that treat each disease independently, which may lead to sub-optimal or even harmful drug combinations. In this work, we propose the LEAP (LEArn to Prescribe) algorithm to decompose the treatment recommendation into a sequential decision-making process while automatically determining the appropriate number of medications. A recurrent decoder is used to model label dependencies and content-based attention is used to capture label instance mapping. We further leverage reinforcement learning to fine tune the model parameters to ensure accuracy and completeness. We incorporate external clinical knowledge into the design of the reinforcement reward to effectively prevent generating unfavorable drug combinations. Both quantitative experiments and qualitative case studies are conducted on two real world electronic health record datasets to verify the effectiveness of our solution. On both datasets, LEAP significantly outperforms baselines by up to 10-30% in terms of mean Jaccard coefficient and removes 99.8% adverse drug interactions in the recommended treatment sets.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1315–1324},
numpages = {10},
keywords = {treatment recommendation, multi-instance multilabel learning, multimorbidity},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098130,
author = {Zhang, Yang and Wang, Yusu and Parthasarathy, Srinivasan},
title = {Visualizing Attributed Graphs via Terrain Metaphor},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098130},
doi = {10.1145/3097983.3098130},
abstract = {The value proposition of a dataset often resides in the implicit interconnections or explicit relationships (patterns) among individual entities, and is often modeled as a graph. Effective visualization of such graphs can lead to key insights uncovering such value. In this article we propose a visualization method to explore attributed graphs with numerical attributes associated with nodes (or edges). Such numerical attributes can represent raw content information, similarities, or derived information reflecting important network measures such as triangle density and centrality. The proposed visualization strategy seeks to simultaneously uncover the relationship between attribute values and graph topology, and relies on transforming the network to generate a terrain map. A key objective here is to ensure that the terrain map reveals the overall distribution of components-of-interest (e.g. dense subgraphs, k-cores) and the relationships among them while being sensitive to the attribute values over the graph. We also design extensions that can capture the relationship across multiple numerical attributes. We demonstrate the efficacy of our method on several real-world data science tasks while scaling to large graphs with millions of nodes.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1325–1334},
numpages = {10},
keywords = {attributed graph, graph visualization},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098167,
author = {Zhang, Lu and Wu, Yongkai and Wu, Xintao},
title = {Achieving Non-Discrimination in Data Release},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098167},
doi = {10.1145/3097983.3098167},
abstract = {Discrimination discovery and prevention/removal are increasingly important tasks in data mining. Discrimination discovery aims to unveil discriminatory practices on the protected attribute (e.g., gender) by analyzing the dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data before conducting predictive analysis. In this paper, we show that the key to discrimination discovery and prevention is to find the meaningful partitions that can be used to provide quantitative evidences for the judgment of discrimination. With the support of the causal graph, we present a graphical condition for identifying a meaningful partition. Based on that, we develop a simple criterion for the claim of non-discrimination, and propose discrimination removal algorithms which accurately remove discrimination while retaining good data utility. Experiments using real datasets show the effectiveness of our approaches.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1335–1344},
numpages = {10},
keywords = {causal graph, discrimination discovery and removal},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098070,
author = {Albert, Adrian and Kaur, Jasleen and Gonzalez, Marta C.},
title = {Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098070},
doi = {10.1145/3097983.3098070},
abstract = {Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1357–1366},
numpages = {10},
keywords = {land use classification, computer vision, satellite imagery, convolutional neural networks},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098045,
author = {Aoki, Raquel Y.S. and Assuncao, Renato M. and Vaz de Melo, Pedro O.S.},
title = {Luck is Hard to Beat: The Difficulty of Sports Prediction},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098045},
doi = {10.1145/3097983.3098045},
abstract = {Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This indicates the relative presence of luck and skill. We collected and analyzed all games from 198 sports leagues comprising 1503 seasons from 84 countries of 4 different sports: basketball, soccer, volleyball and handball. We measured the competitiveness by countries and sports. We also identify in each season which teams, if removed from its league, result in a completely random tournament. Surprisingly, not many of them are needed. As another contribution of this paper, we propose a probabilistic graphical model to learn about the teams' skills and to decompose the relative weights of luck and skill in each game. We break down the skill component into factors associated with the teams' characteristics. The model also allows to estimate as 0.36 the probability that an underdog team wins in the NBA league, with a home advantage adding 0.09 to this probability. As shown in the first part of the paper, luck is substantially present even in the most competitive championships, which partially explains why sophisticated and complex feature-based models hardly beat simple models in the task of forecasting sports' outcomes.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1367–1376},
numpages = {10},
keywords = {graphical model, sports analytics, uncertainty quantification},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098056,
author = {Bao, Jie and He, Tianfu and Ruan, Sijie and Li, Yanhua and Zheng, Yu},
title = {Planning Bike Lanes Based on Sharing-Bikes' Trajectories},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098056},
doi = {10.1145/3097983.3098056},
abstract = {Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization.In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1377–1386},
numpages = {10},
keywords = {trajectory data mining, urban planning, urban computing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098021,
author = {Baylor, Denis and Breck, Eric and Cheng, Heng-Tze and Fiedel, Noah and Foo, Chuan Yu and Haque, Zakaria and Haykal, Salem and Ispir, Mustafa and Jain, Vihan and Koc, Levent and Koo, Chiu Yuen and Lew, Lukasz and Mewald, Clemens and Modi, Akshay Naresh and Polyzotis, Neoklis and Ramesh, Sukriti and Roy, Sudip and Whang, Steven Euijong and Wicke, Martin and Wilkiewicz, Jarek and Zhang, Xin and Zinkevich, Martin},
title = {TFX: A TensorFlow-Based Production-Scale Machine Learning Platform},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098021},
doi = {10.1145/3097983.3098021},
abstract = {Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components---a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt.We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes disruptions.We present the case study of one deployment of TFX in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying TFX led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1387–1395},
numpages = {9},
keywords = {end-to-end platform, large-scale machine learning, continuous training},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098028,
author = {Borisyuk, Fedor and Zhang, Liang and Kenthapadi, Krishnaram},
title = {LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098028},
doi = {10.1145/3097983.3098028},
abstract = {Online professional social networks such as LinkedIn serve as a marketplace, wherein job seekers can find right career opportunities and job providers can reach out to potential candidates. LinkedIn's job recommendations product is a key vehicle for efficient matching between potential candidates and job postings. However, we have observed in practice that a subset of job postings receive too many applications (due to several reasons such as the popularity of the company, nature of the job, etc.), while some other job postings receive too few applications. Both cases can result in job poster dissatisfaction and may lead to discontinuation of the associated job posting contracts. At the same time, if too many job seekers compete for the same job posting, each job seeker's chance of getting this job will be reduced. In the long term, this reduces the chance of users finding jobs that they really like on the site. Therefore, it becomes beneficial for the job recommendation system to consider values provided to both job seekers as well as job posters in the marketplace.In this paper, we propose the job application redistribution problem, with the goal of ensuring that job postings do not receive too many or too few applications, while still providing job recommendations to users with the same level of relevance. We present a dynamic forecasting model to estimate the expected number of applications at the job expiration date, and algorithms to either promote or penalize jobs based on the output of the forecasting model. We also describe the system design and architecture for LiJAR, LinkedIn's Job Applications Forecasting and Redistribution system, which we have implemented and deployed in production. We perform extensive evaluation of LiJAR through both offline and online A/B testing experiments. Our production deployment of this system as part of LinkedIn's job recommendation engine has resulted in significant increase in the engagement of users for underserved jobs (6.5%) without affecting the user engagement in terms of the total number of job applications, thereby addressing the needs of job seekers as well as job providers simultaneously.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1397–1406},
numpages = {10},
keywords = {job application forecasting, personalized search and recommendation systems, job application redistribution},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098078,
author = {Chojnacki, Alex and Dai, Chengyu and Farahi, Arya and Shi, Guangsha and Webb, Jared and Zhang, Daniel T. and Abernethy, Jacob and Schwartz, Eric},
title = {A Data Science Approach to Understanding Residential Water Contamination in Flint},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098078},
doi = {10.1145/3097983.3098078},
abstract = {When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system.In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by Google.org. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1407–1416},
numpages = {10},
keywords = {water quality, risk assessment, flint water crisis, machine learning, sampling bias, public policy},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098042,
author = {Curtis, Ross E. and Girshick, Ahna R.},
title = {Estimation of Recent Ancestral Origins of Individuals on a Large Scale},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098042},
doi = {10.1145/3097983.3098042},
abstract = {The last ten years have seen an exponential growth of direct-to-consumer genomics. One popular feature of these tests is the report of a distant ancestral inference profile-a breakdown of the regions of the world where the test-taker's ancestors may have lived. While current methods and products generally focus on the more distant past (e.g., thousands of years ago), we have recently demonstrated that by leveraging network analysis tools such as community detection, more recent ancestry can be identified. However, using a network analysis tool like community detection on a large network with potentially millions of nodes is not feasible in a live production environment where hundreds or thousands of new genotypes are processed every day. In this study, we describe a classification method that leverages network features to assign individuals to communities in a large network corresponding to recent ancestry. We recently launched a beta version of this research as a new product feature at AncestryDNA.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1417–1425},
numpages = {9},
keywords = {random forest classification, community detection, computational genomics, applied machine learning, scalability of large systems},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098024,
author = {Dmitriev, Pavel and Gupta, Somit and Kim, Dong Woo and Vaz, Garnet},
title = {A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098024},
doi = {10.1145/3097983.3098024},
abstract = {Online controlled experiments (e.g., A/B tests) are now regularly used to guide product development and accelerate innovation in software. Product ideas are evaluated as scientific hypotheses, and tested in web sites, mobile applications, desktop applications, services, and operating systems. One of the key challenges for organizations that run controlled experiments is to come up with the right set of metrics [1] [2] [3]. Having good metrics, however, is not enough.In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment's outcome, which if deployed could hurt the business by millions of dollars. Inspired by Steven Goodman's twelve p-value misconceptions [4], in this paper, we share twelve common metric interpretation pitfalls which we observed repeatedly in our experiments. We illustrate each pitfall with a puzzling example from a real experiment, and describe processes, metric design principles, and guidelines that can be used to detect and avoid the pitfall.With this paper, we aim to increase the experimenters' awareness of metric interpretation issues, leading to improved quality and trustworthiness of experiment results and better data-driven decisions.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1427–1436},
numpages = {10},
keywords = {online experiments, controlled experiments, metrics, a/b testing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098016,
author = {Dong, Yuxiao and Ma, Hao and Shen, Zhihong and Wang, Kuansan},
title = {A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098016},
doi = {10.1145/3097983.3098016},
abstract = {Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1437–1446},
numpages = {10},
keywords = {funding policy, science of science, scientific impact, microsoft academic graph, diversity in science, big data},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098040,
author = {Du, Boxin and Zhang, Si and Cao, Nan and Tong, Hanghang},
title = {FIRST: Fast Interactive Attributed Subgraph Matching},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098040},
doi = {10.1145/3097983.3098040},
abstract = {Attributed subgraph matching is a powerful tool for explorative mining of large attributed networks. In many applications (e.g., network science of teams, intelligence analysis, finance informatics), the user might not know what exactly s/he is looking for, and thus require the user to constantly revise the initial query graph based on what s/he finds from the current matching results. A major bottleneck in such an interactive matching scenario is the efficiency, as simply rerunning the matching algorithm on the revised query graph is computationally prohibitive. In this paper, we propose a family of effective and efficient algorithms (FIRST) to support interactive attributed subgraph matching. There are two key ideas behind the proposed methods. The first is to recast the attributed subgraph matching problem as a cross-network node similarity problem, whose major computation lies in solving a Sylvester equation for the query graph and the underlying data graph. The second key idea is to explore the smoothness between the initial and revised queries, which allows us to solve the new/updated Sylvester equation incrementally, without re-solving it from scratch. Experimental results show that our method can achieve (1) up to 16x speed-up when applying on networks with 6M$+$ nodes; (2) preserving more than 90% accuracy compared with existing methods; and (3) scales linearly with respect to the size of the data graph.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1447–1456},
numpages = {10},
keywords = {social networks, large scale data mining, graph mining, multimedia, healthcare},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098065,
author = {Emrani, Saba and McGuirk, Anya and Xiao, Wei},
title = {Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098065},
doi = {10.1145/3097983.3098065},
abstract = {Parkinson's disease (PD) is a debilitating neurodegenerative disease excessively affecting millions of patients. Early diagnosis of PD is critical as manifestation of symptoms occur many years after the onset of neurodegenration, when more than 60% of dopaminergic neurons are lost. Since there is no definite diagnosis of PD, the early management of disease is a significant challenge in the field of PD therapeutics. Therefore, identifying valid biomarkers that can characterize the progression of PD has lately received growing attentions in PD research community. In this paper, we employ a multi-task learning regression framework for prediction of Parkinson's disease progression, where each task is the prediction of PD rating scales at one future time point. We then use the model to identify the important biomarkers predictive of disease progression. We adopt a graph regularization approach to capture the relationship between different tasks and penalize large variations of the model at consecutive future time points. We have carried out comprehensive experiments using different categories of measurements at baseline from Parkinson's Progression Markers Initiative (PPMI) database to predict the severity of PD, measured by unified PD rating scale. We use the learned model to identify the biomarkers with significant contribution in prediction of PD progression. Our results confirm some of the important biomarkers identified in existing medical studies, validate some of the biomarkers that have been observed as a potential marker of PD and discover new biomarkers that have not yet been investigated.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1457–1466},
numpages = {10},
keywords = {multi-task learning, regression, biomarker identification, parkinson's disease},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098013,
author = {Gan, Guojun and Huang, Jimmy Xiangji},
title = {A Data Mining Framework for Valuing Large Portfolios of Variable Annuities},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098013},
doi = {10.1145/3097983.3098013},
abstract = {A variable annuity is a tax-deferred retirement vehicle created to address concerns that many people have about outliving their assets. In the past decade, the rapid growth of variable annuities has posed great challenges to insurance companies especially when it comes to valuing the complex guarantees embedded in these products.In this paper, we propose a novel data mining framework to address the computational issue associated with the valuation of large portfolios of variable annuity contracts. The data mining framework consists of two major components: a data clustering algorithm which is used to select representative variable annuity contracts, and a regression model which is used to predict quantities of interest for the whole portfolio based on the representative contracts. A series of numerical experiments are conducted on a portfolio of synthetic variable annuity contracts to demonstrate the performance of our proposed data mining framework in terms of accuracy and speed. The experimental results show that our proposed framework is able to produce accurate estimates of various quantities of interest and can reduce the runtime significantly.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1467–1475},
numpages = {9},
keywords = {data mining, kriging, data clustering, portfolio valuation, variable annuity},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098073,
author = {Ghosh, Saurav and Chakraborty, Prithwish and Lewis, Bryan L. and Majumder, Maimuna S. and Cohn, Emily and Brownstein, John S. and Marathe, Madhav V. and Ramakrishnan, Naren},
title = {GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098073},
doi = {10.1145/3097983.3098073},
abstract = {Real-time monitoring and responses to emerging public health threats rely on the availability of timely surveillance data. During the early stages of an epidemic, the ready availability of line lists with detailed tabular information about laboratory-confirmed cases can assist epidemiologists in making reliable inferences and forecasts. Such inferences are crucial to understand the epidemiology of a specific disease early enough to stop or control the outbreak. However, construction of such line lists requires considerable human supervision and therefore, difficult to generate in real-time. In this paper, we motivate Guided Epidemiological Line List (GELL), the first tool for building automated line lists (in near real-time) from open source reports of emerging disease outbreaks. Specifically, we focus on deriving epidemiological characteristics of an emerging disease and the affected population from reports of illness. GELL uses distributed vector representations (ala word2vec) to discover a set of indicators for each line list feature. This discovery of indicators is followed by the use of dependency parsing based techniques for final extraction in tabular form. We evaluate the performance of GELL against a human annotated line list provided by HealthMap corresponding to MERS outbreaks in Saudi Arabia. We demonstrate that GELL extracts line list features with increased accuracy compared to a baseline method. We further show how these automatically extracted line list features can be used for making epidemiological inferences, such as inferring demographics and symptoms-to-hospitalization period of affected individuals.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1477–1485},
numpages = {9},
keywords = {word embeddings, epidemiological line lists, information extraction, negation detection, dependency parsing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098043,
author = {Golovin, Daniel and Solnik, Benjamin and Moitra, Subhodeep and Kochanski, Greg and Karro, John and Sculley, D.},
title = {Google Vizier: A Service for Black-Box Optimization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098043},
doi = {10.1145/3097983.3098043},
abstract = {Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. In this paper we describe Google Vizier, a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google. Google Vizier is used to optimize many of our machine learning models and other systems, and also provides core capabilities to Google's Cloud Machine Learning HyperTune subsystem. We discuss our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning and automated early stopping that the service provides.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1487–1495},
numpages = {9},
keywords = {machine learning, gaussian process, black-box optimization, bayesian inference},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098064,
author = {Gong, Jen J. and Naumann, Tristan and Szolovits, Peter and Guttag, John V.},
title = {Predicting Clinical Outcomes Across Changing Electronic Health Record Systems},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098064},
doi = {10.1145/3097983.3098064},
abstract = {Existing machine learning methods typically assume consistency in how semantically equivalent information is encoded. However, the way information is recorded in databases differs across institutions and over time, often rendering potentially useful data obsolescent. To address this problem, we map database-specific representations of information to a shared set of semantic concepts, thus allowing models to be built from or transition across different databases. We demonstrate our method on machine learning models developed in a healthcare setting. In particular, we evaluate our method using two different intensive care unit (ICU) databases and on two clinically relevant tasks, in-hospital mortality and prolonged length of stay. For both outcomes, a feature representation mapping EHR-specific events to a shared set of clinical concepts yields better results than using EHR-specific events alone.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1497–1505},
numpages = {9},
keywords = {model portability, machine learning, clinical risk models, electronic health records},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098026,
author = {Hou, Shifu and Ye, Yanfang and Song, Yangqiu and Abdulhayoglu, Melih},
title = {HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098026},
doi = {10.1145/3097983.3098026},
abstract = {With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more effort for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1507–1515},
numpages = {9},
keywords = {relation analysis, android malware detection, application programming interface calls, heterogeneous information network},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097992,
author = {Johari, Ramesh and Koomen, Pete and Pekelis, Leonid and Walsh, David},
title = {Peeking at A/B Tests: Why It Matters, and What to Do about It},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097992},
doi = {10.1145/3097983.3097992},
abstract = {This paper reports on novel statistical methodology, which has been deployed by the commercial A/B testing platform Optimizely to communicate experimental results to their customers. Our methodology addresses the issue that traditional p-values and confidence intervals give unreliable inference. This is because users of A/B testing software are known to continuously monitor these measures as the experiment is running. We provide always valid p-values and confidence intervals that are provably robust to this effect. Not only does this make it safe for a user to continuously monitor, but it empowers her to detect true effects more efficiently. This paper provides simulations and numerical studies on Optimizely's data, demonstrating an improvement in detection performance over traditional methods.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1517–1525},
numpages = {9},
keywords = {confidence intervals, a/b testing, sequential hypothesis testing, p-values},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098076,
author = {Koutra, Danai and Dighe, Abhilash and Bhagat, Smriti and Weinsberg, Udi and Ioannidis, Stratis and Faloutsos, Christos and Bolot, Jean},
title = {PNP: Fast Path Ensemble Method for Movie Design},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098076},
doi = {10.1145/3097983.3098076},
abstract = {How can we design a product or movie that will attract, for example, the interest of Pennsylvania adolescents or liberal newspaper critics? What should be the genre of that movie and who should be in the cast? In this work, we seek to identify how we can design new movies with features tailored to a specific user population. We formulate the movie design as an optimization problem over the inference of user-feature scores and selection of the features that maximize the number of attracted users. Our approach, PNP, is based on a heterogeneous, tripartite graph of users, movies, and features (e.g. actors, directors, genres), where users rate movies and features contribute to movies. We learn the preferences by leveraging user similarities defined through different types of relations, and show that our method outperforms state-of-the-art approaches, including matrix factorization and other heterogeneous graph-based analysis. We evaluate PNP on publicly available real-world data and show that it is highly scalable and effectively provides movie designs oriented towards different groups of users, including men, women, and adolescents.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1527–1536},
numpages = {10},
keywords = {product design, user preferences, movies, user modeling, heterogeneous networks, recommendations},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097998,
author = {Kuang, Zhaobin and Peissig, Peggy and Santos Costa, Vitor and Maclin, Richard and Page, David},
title = {Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097998},
doi = {10.1145/3097983.3097998},
abstract = {Several prominent public health incidents that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV), the science and activities to monitor and prevent adverse events caused by pharmaceutical products after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Multiple Self-Controlled Case Series (MSCCS) model, arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times. We apply the proposed method as well as MSCCS to the Marshfield Clinic EHR. Experimental results suggest that incorporating the heterogeneity among different patients and different times help to improve the performance in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1537–1546},
numpages = {10},
keywords = {adverse drug event discovery, electronic health records, longitudinal data, baseline regularization, pharmacovigilance},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098022,
author = {Li, Tao and Jiang, Yexi and Zeng, Chunqiu and Xia, Bin and Liu, Zheng and Zhou, Wubai and Zhu, Xiaolong and Wang, Wentao and Zhang, Liang and Wu, Jun and Xue, Li and Bao, Dewei},
title = {FLAP: An End-to-End Event Log Analysis Platform for System Management},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098022},
doi = {10.1145/3097983.3098022},
abstract = {Many systems, such as distributed operating systems, complex networks, and high throughput web-based applications, are continuously generating large volume of event logs. These logs contain useful information to help system administrators to understand the system running status and to pinpoint the system failures. Generally, due to the scale and complexity of modern systems, the generated logs are beyond the analytic power of human beings. Therefore, it is imperative to develop a comprehensive log analysis system to support effective system management. Although a number of log mining techniques have been proposed to address specific log analysis use cases, few research and industrial efforts have been paid on providing integrated systems with an end-to-end solution to facilitate the log analysis routines.In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs. FLAP provides an end-to-end solution that utilizes advanced data mining techniques to assist log analysts to conveniently, timely, and accurately conduct event log knowledge discovery, system status investigation, and system failure diagnosis. Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly. Since 2016, FLAP has been used by Huawei Technologies Co. Ltd for internal event log analysis, and has provided effective support in its system operation and workflow optimization.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1547–1556},
numpages = {10},
keywords = {log processing, event summarization, event mining, data mining system},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098011,
author = {Liu, Shichen and Xiao, Fei and Ou, Wenwu and Si, Luo},
title = {Cascade Ranking for Operational E-Commerce Search},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098011},
doi = {10.1145/3097983.3098011},
abstract = {In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries.This paper presents novel research and thorough study of designing and deploying a Cascade model in a Large-scale Operational E-commerce Search application (CLOES), which deals with hundreds of millions of user queries per day with hundreds of servers. The challenge of the real-world application provides new insights for research: 1). Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2). Effectiveness of e-commerce search involves multiple types of user behaviors such as click and purchase, while most existing cascade ranking in search only models the click behavior. Based on these observations, a novel cascade ranking model is designed and deployed in an operational e-commerce search application. An extensive set of experiments demonstrate the advantage of the proposed work to address multiple factors of effectiveness, efficiency and user experience in the real-world application.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1557–1565},
numpages = {9},
keywords = {user experience, operational e-commerce search system, effectiveness and efficiency, cascade ranking},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098075,
author = {McNamara, Quinten and De La Vega, Alejandro and Yarkoni, Tal},
title = {Developing a Comprehensive Framework for Multimodal Feature Extraction},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098075},
doi = {10.1145/3097983.3098075},
abstract = {Feature extraction is a critical component of many applied data science workflows. In recent years, rapid advances in artificial intelligence and machine learning have led to an explosion of feature extraction tools and services that allow data scientists to cheaply and effectively annotate their data along a vast array of dimensions--ranging from detecting faces in images to analyzing the sentiment expressed in coherent text. Unfortunately, the proliferation of powerful feature extraction services has been mirrored by a corresponding expansion in the number of distinct interfaces to feature extraction services. In a world where nearly every new service has its own API, documentation, and/or client library, data scientists who need to combine diverse features obtained from multiple sources are often forced to write and maintain ever more elaborate feature extraction pipelines. To address this challenge, we introduce a new open-source framework for comprehensive multimodal feature extraction. Pliers is an open-source Python package that supports standardized annotation of diverse data types (videos, images, audio, and text), and is expressly implemented with both ease-of-use and extensibility in mind. Users can apply a wide range of pre-existing feature extraction tools to their data in just a few lines of Python code, and can also easily add their own custom extractors by writing modular classes. A graph-based API enables rapid development of feature extraction pipelines that output results in a single, standardized format. We describe the package's architecture, detail its advantages over previous feature extraction toolboxes, and use a sample application to a large functional MRI dataset to illustrate how pliers can significantly reduce the time and effort required to construct simple feature extraction workflows while increasing code clarity and maintainability.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1567–1574},
numpages = {8},
keywords = {feature extraction, multimodal retrieval, python, standardization, wrappers},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098005,
author = {Mottini, Alejandro and Acuna-Agost, Rodrigo},
title = {Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098005},
doi = {10.1145/3097983.3098005},
abstract = {Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Some common applications are not only filtering and sorting alternatives, but also changing certain attributes in real-time (e.g., changing the price). In this paper, we concentrate with the problem of modeling air passenger choices of flight itineraries. This problem has historically been tackled using classical Discrete Choice Modelling techniques. Traditional statistical approaches, in particular the Multinomial Logit model (MNL), is widely used in industrial applications due to its simplicity and general good performance. However, MNL models present several shortcomings and assumptions that might not hold in real applications. To overcome these difficulties, we present a new choice model based on Pointer Networks. Given an input sequence, this type of deep neural architecture combines Recurrent Neural Networks with the Attention Mechanism to learn the conditional probability of an output whose values correspond to positions in an input sequence. Therefore, given a sequence of different alternatives presented to a customer, the model can learn to point to the one most likely to be chosen by the customer. The proposed method was evaluated on a real dataset that combines on-line user search logs and airline flight bookings. Experimental results show that the proposed model outperforms the traditional MNL model on several metrics.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1575–1583},
numpages = {9},
keywords = {recurrent neural networks, pointer networks, discrete choice modeling},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098053,
author = {Paul, Debjyoti and Li, Feifei and Teja, Murali Krishna and Yu, Xin and Frost, Richie},
title = {Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says!},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098053},
doi = {10.1145/3097983.3098053},
abstract = {With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to important and emerging social issues and events through social media data is increasingly an important problem. However, there are numerous challenges towards realizing this goal effectively and efficiently, due to the unstructured and noisy nature of social media data. The large volume of the underlying data also presents a fundamental challenge. Furthermore, in many application scenarios, it is often interesting, and in some cases critical, to discover patterns and trends based on geographical and/or temporal partitions, and keep track of how they will change overtime.This brings up the interesting problem of spatio-temporal sentiment analysis from large-scale social media data. This paper investigates this problem through a data science project called "US Election 2016, What Twitter Says". The objective is to discover sentiment on Twitter towards either the democratic or the republican party at US county and state levels over any arbitrary temporal intervals, using a large collection of geotagged tweets from a period of 6 months leading up to the US Presidential Election in 2016. Our results demonstrate that by integrating and developing a combination of machine learning and data management techniques, it ispossible to do this at scale with effective outcomes. The results of our project have the potential to be adapted towards solving and influencing other interesting social issues such as building neighborhood happiness and health indicators.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1585–1594},
numpages = {10},
keywords = {bursty events, spatio-temporal, sentiment analysis, crowd sentiment, scalable framework, election},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098082,
author = {Portnoff, Rebecca S. and Huang, Danny Yuxing and Doerfler, Periwinkle and Afroz, Sadia and McCoy, Damon},
title = {Backpage and Bitcoin: Uncovering Human Traffickers},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098082},
doi = {10.1145/3097983.3098082},
abstract = {Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1595–1604},
numpages = {10},
keywords = {human trafficking, machine learning, bitcoin, security},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098051,
author = {Power, Paul and Ruiz, Hector and Wei, Xinyu and Lucey, Patrick},
title = {Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098051},
doi = {10.1145/3097983.3098051},
abstract = {In soccer, the most frequent event that occurs is a pass. For a trained eye, there are a myriad of adjectives which could describe this event (e.g., "majestic pass", "conservative" to "poor-ball"). However, as these events are needed to be coded live and in real-time (most often by human annotators), the current method of grading passes is restricted to the binary labels 0 (unsuccessful) or 1 (successful). Obviously, this is sub-optimal because the quality of a pass needs to be measured on a continuous spectrum (i.e., 0 to 100%) and not a binary value. Additionally, a pass can be measured across multiple dimensions, namely: i) risk -- the likelihood of executing a pass in a given situation, and ii) reward -- the likelihood of a pass creating a chance. In this paper, we show how we estimate both the risk and reward of a pass across two seasons of tracking data captured from a recent professional soccer league with state-of-the-art performance, then showcase various use cases of our deployed passing system.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1605–1613},
numpages = {9},
keywords = {clustering, unsupervised, passing, soccer, tracking data},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097986,
author = {Qin, Xiao and Kakar, Tabassum and Wunnava, Susmitha and Rundensteiner, Elke A. and Cao, Lei},
title = {MARAS: Signaling Multi-Drug Adverse Reactions},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097986},
doi = {10.1145/3097983.3097986},
abstract = {There is a growing need for computing-supported methods that facilitate the automated signaling of Adverse Drug Reactions (ADRs) otherwise left undiscovered from the exploding amount of ADR reports filed by patients, medical professionals and drug manufacturers. In this research, we design a Multi-Drug Adverse Reaction Analytics Strategy, called MARAS, to signal severe unknown ADRs triggered by the usage of a combination of drugs, also known as Multi-Drug Adverse Reactions (MDAR). First, MARAS features an efficient signal generation algorithm based on association rule learning that extracts non-spurious MDAR associations. Second, MARAS incorporates contextual information to detect drug combinations that are strongly associated with a set of ADRs. It groups related associations into Contextual Association Clusters (CACs) that then avail contextual information to evaluate the significance of the discovered MDAR Associations. Lastly, we use this contextual significance to rank discoveries by their notion of interestingness to signal the most compelling MDARs. To demonstrate the utility of MARAS, it is compared with state-of-the-art techniques and evaluated via case studies on datasets collected by U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1615–1623},
numpages = {9},
keywords = {public health surveillance, interestingness of association, association rule learning, adverse drug reaction},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098041,
author = {Shah, Parikshit and Yang, Ming and Alle, Sachidanand and Ratnaparkhi, Adwait and Shahshahani, Ben and Chandra, Rohit},
title = {A Practical Exploration System for Search Advertising},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098041},
doi = {10.1145/3097983.3098041},
abstract = {In this paper, we describe an exploration system that was implemented by the search-advertising team of a prominent web-portal to address the cold ads problem. The cold ads problem refers to the situation where, when new ads are injected into the system by advertisers, the system is unable to assign an accurate quality to the ad (in our case, the click probability). As a consequence, the advertiser may suffer from low impression volumes for these cold ads, and the overall system may perform sub-optimally if the click probabilities for new ads are not learnt rapidly. We designed a new exploration system that was adapted to search advertising and the serving constraints of the system. In this paper, we define the problem, discuss the design details of the exploration system, new evaluation criteria, and present the performance metrics that were observed by us.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1625–1631},
numpages = {7},
keywords = {cold ads, exploration, search advertising},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098057,
author = {Sybrandt, Justin and Shtutman, Michael and Safro, Ilya},
title = {MOLIERE: Automatic Biomedical Hypothesis Generation System},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098057},
doi = {10.1145/3097983.3098057},
abstract = {Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents and does not limit the document vocabulary. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network. We demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1633–1642},
numpages = {10},
keywords = {topic modeling, hypothesis generation, mining scientific publications, undiscovered public knowledge},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098048,
author = {Tata, Sandeep and Popescul, Alexandrin and Najork, Marc and Colagrosso, Mike and Gibbons, Julian and Green, Alan and Mah, Alexandre and Smith, Michael and Garg, Divanshu and Meyer, Cayden and Kan, Reuben},
title = {Quick Access: Building a Smart Experience for Google Drive},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098048},
doi = {10.1145/3097983.3098048},
abstract = {Google Drive is a cloud storage and collaboration service used by hundreds of millions of users around the world. Quick Access is a new feature in Google Drive that surfaces the most relevant documents when a user visits the home screen. Our metrics show that users locate their documents in half the time with this feature compared to previous approaches. The development of Quick Access illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine learning in mind, and evolving product definitions. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine learning problems.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1643–1651},
numpages = {9},
keywords = {private data, neural networks, applied machine learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098018,
author = {Tong, Yongxin and Chen, Yuqiang and Zhou, Zimu and Chen, Lei and Wang, Jie and Yang, Qiang and Ye, Jieping and Lv, Weifeng},
title = {The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands Based on Large-Scale Online Platforms},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098018},
doi = {10.1145/3097983.3098018},
abstract = {Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1653–1662},
numpages = {10},
keywords = {prediction, feature engineering, unit original taxi demands},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098004,
author = {Vandal, Thomas and Kodra, Evan and Ganguly, Sangram and Michaelis, Andrew and Nemani, Ramakrishna and Ganguly, Auroop R.},
title = {DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098004},
doi = {10.1145/3097983.3098004},
abstract = {The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1663–1672},
numpages = {10},
keywords = {deep learning, daily precipitation, super-resolution, climate statistical downscaling},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3097985,
author = {Wang, Jingyuan and Chen, Chao and Wu, Junjie and Xiong, Zhang},
title = {No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3097985},
doi = {10.1145/3097983.3097985},
abstract = {Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly "bomb" we sleep with every day. In both academia and government, tremendous efforts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the problem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features a "duet" between DGT trajectory data and human mobility data for risky zones identification. Moreover, DGeye innovatively takes risky patterns as the keystones in DGT management, and builds causality networks among them for pain point identification, attribution and prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye. In particular, the report generated by DGeye driven the Beijing government to lay down gas pipelines for the famous Guijie food street.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1673–1681},
numpages = {9},
keywords = {urban safety, pattern mining, causal analysis, intelligent transportation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098058,
author = {Zhang, Zhe and Li, Beibei},
title = {A Quasi-Experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098058},
doi = {10.1145/3097983.3098058},
abstract = {With the pervasiveness of mobile technology and location-based computing, new forms of smart urban transportation, such as Uber & Lyft, have become increasingly popular. These new forms of urban infrastructure can influence individuals' movement frictions and patterns, in turn influencing local consumption patterns and the economic performance of local businesses. To gain insights about future impact of urban transportation changes, in this paper, we utilize a novel dataset and econometric analysis methods to present a quasi-experimental examination of how the emerging growth of peer-to-peer car sharing services may have affected local consumer mobility and consumption patterns.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1683–1692},
numpages = {10},
keywords = {lyft, econometrics, societal impact of technolgy, uber, sharing economy, transportation, offline consumption},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098029,
author = {Zhou, Jun and Li, Xiaolong and Zhao, Peilin and Chen, Chaochao and Li, Longfei and Yang, Xinxing and Cui, Qing and Yu, Jin and Chen, Xu and Ding, Yi and Qi, Yuan Alan},
title = {KunPeng: Parameter Server Based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098029},
doi = {10.1145/3097983.3098029},
abstract = {In recent years, due to the emergence of Big Data (terabytes or petabytes) and Big Model (tens of billions of parameters), there has been an ever-increasing need of parallelizing machine learning (ML) algorithms in both academia and industry. Although there are some existing distributed computing systems, such as Hadoop and Spark, for parallelizing ML algorithms, they only provide synchronous and coarse-grained operators (e.g., Map, Reduce, and Join, etc.), which may hinder developers from implementing more efficient algorithms. This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML. Specifically, KunPeng not only encapsulates the characteristics of data/model parallelism, load balancing, model sync-up, sparse representation, industrial fault-tolerance, etc., but also provides easy-to-use interface to empower users to focus on the core ML logics. Empirical results on terabytes of real datasets with billions of samples and features demonstrate that, such a design brings compelling performance improvements on ML programs ranging from Follow-the-Regularized-Leader Proximal algorithm to Sparse Logistic Regression and Multiple Additive Regression Trees. Furthermore, KunPeng's encouraging performance is also shown for several real-world applications including the Alibaba's Double 11 Online Shopping Festival and Ant Financial's transaction risk estimation.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1693–1702},
numpages = {10},
keywords = {distributed computing system, parameter server, distributed optimization algorithm},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098059,
author = {Zhu, Jie and Shan, Ying and Mao, J.C. and Yu, Dong and Rahmanian, Holakou and Zhang, Yi},
title = {Deep Embedding Forest: Forest-Based Serving with Deep Embedding Features},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098059},
doi = {10.1145/3097983.3098059},
abstract = {Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving.Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1703–1711},
numpages = {9},
keywords = {xgboost, deep neural network (dnn), cntk, deep learning, lightgbm, deep crossing, gpu, gradient boosting machine},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098200,
author = {Ahmed, Amr and Long, James and Silva, Daniel and Wang, Yuan},
title = {A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098200},
doi = {10.1145/3097983.3098200},
abstract = {Topic models are often applied in industrial settings to discover user profiles from activity logs where documents correspond to users and words to complex objects such as web sites and installed apps. Standard topic models ignore the content-based similarity structure between these objects largely because of the inability of the Dirichlet prior to capture such side information of word-word correlation. Several approaches were proposed to replace the Dirichlet prior with more expressive alternatives. However, this added expressivity comes with a heavy premium: inference becomes intractable and sparsity is lost which renders these alternatives not suitable for industrial scale applications. In this paper we take a radically different approach to incorporating word-word correlation in topic models by applying this side information at the posterior level rather than at the prior level. We show that this choice preserves sparsity and results in a graph-based sampler for LDA whose computational complexity is asymptotically on bar with the state of the art Alias base sampler for LDA cite{aliasLDA}. We illustrate the efficacy of our approach over real industrial datasets that span up to billion of users, tens of millions of words and thousands of topics. To the best of our knowledge, our approach provides the first practical and scalable solution to this important problem.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1713–1721},
numpages = {9},
keywords = {user modeling, big data, interpretable models, knowledge representation, latent variable models, topic models},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098163,
author = {Anderson, Blake and McGrew, David},
title = {Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098163},
doi = {10.1145/3097983.3098163},
abstract = {The application of machine learning for the detection of malicious network traffic has been well researched over the past several decades; it is particularly appealing when the traffic is encrypted because traditional pattern-matching approaches cannot be used. Unfortunately, the promise of machine learning has been slow to materialize in the network security domain. In this paper, we highlight two primary reasons why this is the case: inaccurate ground truth and a highly non-stationary data distribution. To demonstrate and understand the effect that these pitfalls have on popular machine learning algorithms, we design and carry out experiments that show how six common algorithms perform when confronted with real network data. With our experimental results, we identify the situations in which certain classes of algorithms underperform on the task of encrypted malware traffic classification. We offer concrete recommendations for practitioners given the real-world constraints outlined. From an algorithmic perspective, we find that the random forest ensemble method outperformed competing methods. More importantly, feature engineering was decisive; we found that iterating on the initial feature set, and including features suggested by domain experts, had a much greater impact on the performance of the classification system. For example, linear regression using the more expressive feature set easily outperformed the random forest method using a standard network traffic representation on all criteria considered. Our analysis is based on millions of TLS encrypted sessions collected over 12 months from a commercial malware sandbox and two geographically distinct, large enterprise networks.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1723–1732},
numpages = {10},
keywords = {malware detection, network security, machine learning, tls},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098139,
author = {Bifet, Albert and Zhang, Jiajin and Fan, Wei and He, Cheng and Zhang, Jianfeng and Qian, Jianfeng and Holmes, Geoff and Pfahringer, Bernhard},
title = {Extremely Fast Decision Tree Mining for Evolving Data Streams},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098139},
doi = {10.1145/3097983.3098139},
abstract = {Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning.In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1733–1742},
numpages = {10},
keywords = {classification, online learning, decision trees, data streams},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098150,
author = {Chahuara, Pedro and Grislain, Nicolas and Jauvion, Gregoire and Renders, Jean-Michel},
title = {Real-Time Optimization of Web Publisher RTB Revenues},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098150},
doi = {10.1145/3097983.3098150},
abstract = {This paper describes an engine to optimize web publisher revenues from second-price auctions. These auctions are widely used to sell online ad spaces in a mechanism called real-time bidding (RTB). Optimization within these auctions is crucial for web publishers, because setting appropriate reserve prices can significantly increase revenue. We consider a practical real-world setting where the only available information before an auction occurs consists of a user identifier and an ad placement identifier. The real-world challenges we had to tackle consist mainly of tracking the dependencies on both the user and placement in an highly non-stationary environment and of dealing with censored bid observations. These challenges led us to make the following design choices: (i) we adopted a relatively simple non-parametric regression model of auction revenue based on an incremental time-weighted matrix factorization which implicitly builds adaptive users' and placements' profiles; (ii) we jointly used a non-parametric model to estimate the first and second bids' distribution when they are censored, based on an on-line extension of the Aalen's Additive model.Our engine is a component of a deployed system handling hundreds of web publishers across the world, serving billions of ads a day to hundreds of millions of visitors. The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond and yields a significant revenue increase for the web publishers.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1743–1751},
numpages = {9},
keywords = {real-time, ad-tech, big-data, online learning, auctions},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098123,
author = {Chamberlain, Benjamin Paul and Cardoso, \^{A}ngelo and Liu, C.H. Bryan and Pagliari, Roberto and Deisenroth, Marc Peter},
title = {Customer Lifetime Value Prediction Using Embeddings},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098123},
doi = {10.1145/3097983.3098123},
abstract = {We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1753–1762},
numpages = {10},
keywords = {e-commerce, neural networks, customer lifetime value, embeddings, random forests},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098171,
author = {Cheng, Heng-Tze and Haque, Zakaria and Hong, Lichan and Ispir, Mustafa and Mewald, Clemens and Polosukhin, Illia and Roumpos, Georgios and Sculley, D. and Smith, Jamie and Soergel, David and Tang, Yuan and Tucker, Philipp and Wicke, Martin and Xia, Cassandra and Xie, Jianwei},
title = {TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098171},
doi = {10.1145/3097983.3098171},
abstract = {We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain-specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide developers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation.We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input dataWe discuss our experience in using this framework in research and production environments, and show the impact on code health, maintainability, and development speed.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1763–1771},
numpages = {9},
keywords = {machine learning framework, deep learning, high level api},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098169,
author = {Dadkhahi, Hamid and Marlin, Benjamin M.},
title = {Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098169},
doi = {10.1145/3097983.3098169},
abstract = {In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node. To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and the delivery of just-in-time adaptive interventions. We evaluate our framework on mobile sensor-based human activity recognition and mobile health detector learning problems.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1773–1781},
numpages = {9},
keywords = {low-power embedded sensing networks, cascaded classification, mobile health},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098157,
author = {Deb, Supratim and Ge, Zihui and Isukapalli, Sastry and Puthenpura, Sarat and Venkataraman, Shobha and Yan, He and Yates, Jennifer},
title = {AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments},
year = {2017},
isbn = {9781450348874},

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr