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NIPS · 24.8k rows

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8,800	Sample Complexity of Learning Mixtures of Sparse Linear Regressions Akshay Krishnamurthy Microsoft Research, NYC akshay@cs.umass.edu Arya Mazumdar UMass Amherst arya@cs.umass.edu Andrew McGregor UMass Amherst mcgregor@cs.umass.edu Soumyabrata Pal UMass Amherst spal@cs.umass.edu Abstract In...	2019	208
8,801	A Convex Relaxation Barrier to Tight Robustness Veriﬁcation of Neural Networks Hadi Salman∗ Microsoft Research AI hadi.salman@microsoft.com Greg Yang Microsoft Research AI gregyang@microsoft.com Huan Zhang UCLA huan@huan-zhang.com Cho-Jui Hsieh UCLA chohsieh@cs.ucla.edu Pengchuan Zhang Mic...	2019	209
8,802	Learning Erd˝os-Rényi Random Graphs via Edge Detecting Queries Zihan Li National University of Singapore lizihan@u.nus.edu Matthias Fresacher University of Adelaide matthias.fresacher@adelaide.edu.au Jonathan Scarlett National University of Singapore scarlett@comp.nus.edu.sg Abstract In this pap...	2019	21
8,803	A Latent Variational Framework for Stochastic Optimization Philippe Casgrain Department of Statistical Sciences University of Toronto Toronto, ON, Canada p.casgrain@mail.utoronto.ca Abstract This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a laten...	2019	210
8,804	Escaping from saddle points on Riemannian manifolds Yue Sun University of Washington Seattle, WA 98105 yuesun@uw.edu Nicolas Flammarion EPFL Lausanne, Switzerland nicolas.flammarion@epfl.ch Maryam Fazel University of Washington Seattle, WA 98105 mfazel@uw.edu Abstract We consider minimizin...	2019	211
8,805	Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed nouiehed@usc.edu ∗ Maziar Sanjabi sanjabi@usc.edu † Tianjian Huang tianjian@usc.edu ‡ Jason D. Lee jasonlee@princeton.edu § Meisam Razaviyayn razaviya@usc.edu ¶ Abstract Recent applications that aris...	2019	212
8,806	The Option Keyboard Combining Skills in Reinforcement Learning André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup {andrebarreto,borsa,shaobohou,gcomanici,eser}@google.com {hamelphi,kenjitoyama,jjhunt,shi...	2019	213
8,807	On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective Lili Su CSAIL, MIT lilisu@mit.edu Pengkun Yang Department of Electrical Engineering Princeton University pengkuny@princeton.edu Abstract We consider training over-parameterized two-layer neural networks with Recti...	2019	214
8,808	Modeling Tabular Data using Conditional GAN Lei Xu MIT LIDS Cambridge, MA leix@mit.edu Maria Skoularidou MRC-BSU, University of Cambridge Cambridge, UK ms2407@cam.ac.uk Alfredo Cuesta-Infante Universidad Rey Juan Carlos Móstoles, Spain alfredo.cuesta@urjc.es Kalyan Veeramachaneni MIT LIDS ...	2019	215
8,809	Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Mila, Université de Montréal Aaron Mishkin University of British Columbia Issam Laradji University of British Columbia Element AI Mark Schmidt University of British Columbia, 1QBit CCAI Afﬁliate Chair (A...	2019	216
8,810	Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies Yonathan Efroni∗ Technion, Israel Nadav Merlis ∗ Technion, Israel Mohammad Ghavamzadeh Facebook AI Research Shie Mannor Technion, Israel Abstract State-of-the-art efﬁcient model-based Reinforcement Learning (RL) algori...	2019	217
8,811	Weighted Linear Bandits for Non-Stationary Environments Yoan Russac CNRS, Inria, ENS, Université PSL yoan.russac@ens.fr Claire Vernade Deepmind vernade@google.com Olivier Cappé CNRS, Inria, ENS, Université PSL olivier.cappe@cnrs.fr Abstract We consider a stochastic linear bandit model in which t...	2019	218
8,812	Neural Lyapunov Control Ya-Chien Chang UCSD yac021@eng.ucsd.edu Nima Roohi UCSD nroohi@eng.ucsd.edu Sicun Gao UCSD sicung@eng.ucsd.edu Abstract We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of st...	2019	219
8,813	Cormorant: Covariant Molecular Neural Networks Brandon Anderson∗‡, Truong-Son Hy∗and Risi Kondor∗†♯ ∗Department of Computer Science, †Department of Statistics The University of Chicago ♯Center for Computational Mathematics, Flatiron Institute ‡ Atomwise {hytruongson,risi}@uchicago.edu brandona@jfi.uchicag...	2019	22
8,814	Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization Adithya M. Devraj∗ and Jianshu Chen† Abstract We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions. Such a pr...	2019	220
8,815	Hamiltonian Neural Networks Sam Greydanus Google Brain sgrey@google.com Misko Dzamba PetCube mouse9911@gmail.com Jason Yosinski Uber AI Labs yosinski@uber.com Abstract Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow the...	2019	221
8,816	Better transfer learning with inferred successor maps Tamas J. Madarasz University of Oxford tamas.madarasz@ndcn.ox.ac.uk Timothy E. Behrens University of Oxford behrens@fmrib.ox.ac.uk Abstract Humans and animals show remarkable ﬂexibility in adjusting their behaviour when their goals, or rewards in t...	2019	222
8,817	Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond Arindam Banerjee Qilong Gu Vidyashankar Sivakumar Zhiwei Steven Wu Department of Computer Science & Engineering, University of Minnesota, Twin Cities Minneapolis, MN 55455, USA Abstract Several important families of...	2019	223
8,818	Energy-Inspired Models: Learning with Sampler-Induced Distributions Dieterich Lawson∗† Stanford University jdlawson@stanford.edu George Tucker∗, Bo Dai Google Research, Brain Team {gjt, bodai}@google.com Rajesh Ranganath New York University rajeshr@cims.nyu.edu Abstract Energy-based models (EBMs...	2019	224
8,819	Data-Dependence of Plateau Phenomenon in Learning with Neural Network — Statistical Mechanical Analysis Yuki Yoshida Masato Okada Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan {yoshida@mns...	2019	225
8,820	Differentiable Cloth Simulation for Inverse Problems Junbang Liang Ming C. Lin University of Maryland, College Park Vladlen Koltun Intel Labs Abstract We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks. This approach provides an effective, robust framewor...	2019	226
8,821	Detecting Overﬁtting via Adversarial Examples Roman Werpachowski András György Csaba Szepesvári DeepMind, London, UK {romanw,agyorgy,szepi}@google.com Abstract The frequent reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whethe...	2019	227
8,822	Region-speciﬁc Diffeomorphic Metric Mapping Zhengyang Shen UNC Chapel Hill zyshen@cs.unc.edu François-Xavier Vialard LIGM, UPEM francois-xavier.vialard@u-pem.fr Marc Niethammer UNC Chapel Hill mn@cs.unc.edu Abstract We introduce a region-speciﬁc diffeomorphic metric mapping (RDMM) registration app...	2019	228
8,823	Teaching Multiple Concepts to a Forgetful Learner Anette Hunziker† Yuxin Chen¶ Oisin Mac Aodha§ Manuel Gomez Rodriguez* Andreas Krause‡ Pietro Perona⋆ Yisong Yue⋆ Adish Singla* †University of Zurich, anette.hunziker@gmail.com, ¶University of Chicago, chenyuxin@uchicago.edu, §University of Edinburg...	2019	229
8,824	Flattening a Hierarchical Clustering through Active Learning Fabio Vitale Department of Computer Science INRIA Lille, France & Sapienza University of Rome, Italy fabio.vitale@inria.fr Anand Rajagopalan Google Research NY New York, USA anandbr@google.com Claudio Gentile Google Research NY New Y...	2019	23
8,825	Domain Generalization via Model-Agnostic Learning of Semantic Features Qi Dou Daniel C. Castro Konstantinos Kamnitsas Ben Glocker Biomedical Image Analysis Group, Imperial College London, UK {qi.dou,dc315,kk2412,b.glocker}@imperial.ac.uk Abstract Generalization capability to unseen domains is crucial ...	2019	230
8,826	Unconstrained Monotonic Neural Networks Antoine Wehenkel University of Liège Gilles Louppe University of Liège Abstract Monotonic neural networks have recently been proposed as a way to deﬁne invertible transformations. These transformations can be combined into powerful autoregressive ﬂows that have been...	2019	231
8,827	Efﬁcient Identiﬁcation in Linear Structural Causal Models with Instrumental Cutsets Daniel Kumor Purdue University dkumor@purdue.edu Bryant Chen Brex Inc. bryant@brex.com Elias Bareinboim Columbia University eb@cs.columbia.edu Abstract One of the most common mistakes made when performing data an...	2019	232
8,828	Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulation Sawyer Birnbaum∗12, Volodymyr Kuleshov∗12, S. Zayd Enam1, Pang Wei Koh1, and Stefano Ermon1 1Stanford University, Stanford, CA 2Afresh Technologies, San Francisco, CA {sawyerb,kuleshov,zayd,pangwei,ermon}@cs.stanford.edu ...	2019	233
8,829	Convolution with even-sized kernels and symmetric padding Shuang Wu1, Guanrui Wang1, Pei Tang1, Feng Chen2, Luping Shi1 1Department of Precision Instrument, 2Department of Automation Center for Brain Inspired Computing Research Beijing Innovation Center for Future Chip Tsinghua University {lpshi,chenfeng}...	2019	234
8,830	Inducing brain-relevant bias in natural language processing models Dan Schwartz Carnegie Mellon University drschwar@cs.cmu.edu Mariya Toneva Carnegie Mellon University mariya@cmu.edu Leila Wehbe Carnegie Mellon University lwehbe@cmu.edu Abstract Progress in natural language processing (NLP) mode...	2019	235
8,831	SMILe : Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies Seyed Kamyar Seyed Ghasemipour University of Toronto Vector Institute kamyar@cs.toronto.edu Shixiang Gu Google Brain shanegu@google.com Richard Zemel University of Toronto Vector Institute zemel@cs.toronto....	2019	236
8,832	Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model Erik Nijkamp UCLA Department of Statistics enijkamp@ucla.edu Mitch Hill UCLA Department of Statistics mkhill@ucla.edu Song-Chun Zhu UCLA Department of Statistics sczhu@stat.ucla.edu Ying Nian Wu UCLA Department of St...	2019	237
8,833	Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks Kohei Hayashi Preferred Networks hayasick@preferred.jp Taiki Yamaguchi˚ The University of Tokyo yamaguchi@hep-th.phys.s.u-tokyo.ac.jp Yohei Sugawara Preferred Networks suga@preferred.jp Shin-ichi Maeda ...	2019	238
8,834	Interval timing in deep reinforcement learning agents Ben Deverett DeepMind bendeverett@google.com Ryan Faulkner DeepMind rfaulk@google.com Meire Fortunato DeepMind meirefortunato@google.com Greg Wayne DeepMind gregwayne@google.com Joel Z. Leibo DeepMind jzl@google.com Abstract The mea...	2019	239
8,835	Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves Stefan Meintrup Faculty of Computer Science TU Dortmund University Dortmund, Germany stefan.meintrup@tu-dortmund.de Alexander Munteanu Dortmund Data Science Center TU Dortmund University Dortmund, Germany ...	2019	24
8,836	Shaping Belief States with Generative Environment Models for RL Karol Gregor Danilo Jimenez Rezende Frederic Besse Yan Wu Hamza Merzic Aäron van den Oord Google DeepMind London, UK {karolg, danilor, fbesse, yanwu, hamzamerzic, avdnoord}@google.com Abstract When agents interact with a complex env...	2019	240
8,837	Uncertainty-based Continual Learning with Adaptive Regularization Hongjoon Ahn1∗, Sungmin Cha2∗, Donggyu Lee2 and Taesup Moon1,2 1 Department of Artiﬁcial Intelligence, 2Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea 16419 {hong0805, csm9493, ldk308, tsmoon}@skku.edu ...	2019	241
8,838	Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu∗, Yizhou Chen∗, Zhongxiang Dai, Bryan Kian Hsiang Low, and Patrick Jaillet† Dept. of Computer Science, National University of Singapore, Republic of Singapore Dept. of Electrical Engineering and Computer Science, MIT, USA† {haibin,...	2019	242
8,839	Are Sixteen Heads Really Better than One? Paul Michel Language Technologies Institute Carnegie Mellon University Pittsburgh, PA pmichel1@cs.cmu.edu Omer Levy Facebook Artiﬁcial Intelligence Research Seattle, WA omerlevy@fb.com Graham Neubig Language Technologies Institute Carnegie Mellon Univers...	2019	243
8,840	Model Compression with Adversarial Robustness: A Uniﬁed Optimization Framework Shupeng Gui⋄,∗, Haotao Wang†,∗, Haichuan Yang⋄, Chen Yu⋄, Zhangyang Wang† and Ji Liu‡ ⋄Department of Computer Science, University of Rochester †Department of Computer Science and Engineering, Texas A&M University ‡Ytech Seattle A...	2019	244
8,841	Subspace Attack: Exploiting Promising Subspaces for Query-Efﬁcient Black-box Attacks Ziang Yan1,3* Yiwen Guo2,3* Changshui Zhang1 1Institute for Artiﬁcial Intelligence, Tsinghua University (THUAI), State Key Lab of Intelligent Technologies and Systems, Beijing National Research Center for Information Scie...	2019	245
8,842	Combinatorial Bayesian Optimization using the Graph Cartesian Product Changyong Oh1 Jakub M. Tomczak2 Efstratios Gavves1 Max Welling1,2,3 1 University of Amsterdam 2 Qualcomm AI Research 3 CIFAR C.Oh@uva.nl, jtomczak@qti.qualcomm.com, egavves@uva.nl, m.welling@uva.nl Abstract This paper focuses on Bayesian ...	2019	246
8,843	Sample Adaptive MCMC Michael H. Zhu Department of Computer Science Stanford University Stanford, CA 94305 mhzhu@cs.stanford.edu Abstract For MCMC methods like Metropolis-Hastings, tuning the proposal distribution is important in practice for effective sampling from the target distribution π. In this p...	2019	247
8,844	Tree-Sliced Variants of Wasserstein Distances Tam Le RIKEN AIP, Japan tam.le@riken.jp Makoto Yamada Kyoto University & RIKEN AIP, Japan makoto.yamada@riken.jp Kenji Fukumizu ISM, Japan & RIKEN AIP, Japan fukumizu@ism.ac.jp Marco Cuturi Google Brain, Paris & CREST - ENSAE cuturi@google.com Abst...	2019	248
8,845	Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems Robert Ness Gamalon Inc. robert.ness@gamalon.com Kaushal Paneri Northeastern University kaushalpaneri@gmail.com Olga Vitek Northeastern University o.vitek@northeastern.edu Abstract Th...	2019	249
8,846	Explicit Explore-Exploit Algorithms in Continuous State Spaces Mikael Henaff Microsoft Research mihenaff@microsoft.com Abstract We present a new model-based algorithm for reinforcement learning (RL) which consists of explicit exploration and exploitation phases, and is applicable in large or inﬁnite sta...	2019	25
8,847	An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng Department of Mathematics Purdue University West Lafayette, IN 47907 deng106@purdue.edu Xiao Zhang Department of Computer Science Purdue University West Lafayette, IN 47907 zhang923@purdue.edu Faming Liang Department of St...	2019	250
8,848	Topology-Preserving Deep Image Segmentation ∗Xiaoling Hu1, Li Fuxin2, Dimitris Samaras1 and Chao Chen1 1Stony Brook University 2Oregon State University Abstract Segmentation algorithms are prone to topological errors on ﬁne-scale structures, e.g., broken connections. We propose a novel method that learns to...	2019	251
8,849	Stacked Capsule Autoencoders Adam R. Kosiorek∗† ‡ adamk@robots.ox.ac.uk Sara Sabour§ Yee Whye Teh∇ Geoffrey E. Hinton§ ‡ Applied AI Lab Oxford Robotics Institute University of Oxford † Department of Statistics University of Oxford § Google Brain Toronto ∇DeepMind London Abstract Objects ...	2019	252
8,850	Progressive Augmentation of GANs Dan Zhang Bosch Center for Artiﬁcial Intelligence dan.zhang2@bosch.com Anna Khoreva Bosch Center for Artiﬁcial Intelligence anna.khoreva@bosch.com Abstract Training of Generative Adversarial Networks (GANs) is notoriously fragile, requiring to maintain a careful balance ...	2019	253
8,851	Online Sampling from Log-Concave Distributions Holden Lee Duke University Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Yale University Abstract Given a sequence of convex functions f0, f1, . . . , fT , we study the problem of sampling from the Gibbs distribution ⇡t / e−Pt k=0 fk...	2019	254
8,852	Practical Two-Step Look-Ahead Bayesian Optimization Jian Wu wujian046@gmail.com Peter I. Frazier⇤ School of Operations Research and Information Engineering Cornell University Ithaca, NY 14850 pf98@cornell.edu Abstract Expected improvement and other acquisition functions widely used in Bayesian optim...	2019	255
8,853	Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer Zhiyong Yang1,2 Qianqian Xu3 Yangbangyan Jiang1,2 Xiaochun Cao1,2,6 Qingming Huang3,4,5,6∗ 1State Key Laboratory of Information Security, Institute of Information Engineering, CAS 2School of Cy...	2019	256
8,854	Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems Young Hun Jung Department of Statistics University of Michigan yhjung@umich.edu Ambuj Tewari Department of Statistics University of Michigan tewaria@umich.edu Abstract Restless bandit problems are instances of non-stationary ...	2019	257
8,855	Adaptive Sequence Submodularity Marko Mitrovic Yale University marko.mitrovic@yale.edu Ehsan Kazemi Yale University ehsan.kazemi@yale.edu Moran Feldman University of Haifa moranfe@openu.ac.il Andreas Krause ETH Z¨urich krausea@ethz.ch Amin Karbasi Yale University amin.karbasi@yale.edu Ab...	2019	258
8,856	N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI {shengchao, demirel, yliang}@cs.wisc.edu Abstract Machine learning techniques have rece...	2019	259
8,857	How degenerate is the parametrization of neural networks with the ReLU activation function? Julius Berner Faculty of Mathematics, University of Vienna Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria julius.berner@univie.ac.at Dennis Elbrächter Faculty of Mathematics, University of Vienna Oskar-Morgenste...	2019	26
8,858	The spiked matrix model with generative priors Benjamin Aubin†, Bruno Loureiro†, Antoine Maillard⋆, Florent Krzakala⋆, Lenka Zdeborová† Abstract Using a low-dimensional parametrization of signals is a generic and powerful way to enhance performance in signal processing and statistical inference. A very popu...	2019	260
8,859	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares Rong Ge 1, Sham M. Kakade 2, Rahul Kidambi3 and Praneeth Netrapalli4 1 Duke University, 2 University of Washington, 3 Cornell University, 4 Microsoft Research, India. rongge@cs.duke.edu, sham@cs.washingto...	2019	261
8,860	Understanding and Improving Layer Normalization Jingjing Xu1, Xu Sun1,2∗, Zhiyuan Zhang1, Guangxiang Zhao2, Junyang Lin1 1 MOE Key Lab of Computational Linguistics, School of EECS, Peking University 2 Center for Data Science, Peking University {jingjingxu,xusun,zzy1210,zhaoguangxiang,linjunyang}@pku.edu.cn Ab...	2019	262
8,861	Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stanford University yangsong@cs.stanford.edu Stefano Ermon Stanford University ermon@cs.stanford.edu Abstract We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the dat...	2019	263
8,862	Hypothesis Set Stability and Generalization Dylan J. Foster Massachusetts Institute of Technology dylanf@mit.edu Spencer Greenberg Spark Wave admin@sparkwave.tech Satyen Kale Google Research satyen@satyenkale.com Haipeng Luo University of Southern California haipengl@usc.edu Mehryar Mohri Go...	2019	264
8,863	Balancing Efﬁciency and Fairness in On-Demand Ridesourcing Nixie S. Lesmana∗ nixiesap001@e.ntu.edu.sg Xuan Zhang† xuan6@illinois.edu Xiaohui Bei∗ xhbei@ntu.edu.sg Abstract We investigate the problem of assigning trip requests to available vehicles in ondemand ridesourcing. Much of the literature has f...	2019	265
8,864	Backprop with Approximate Activations for Memory-efﬁcient Network Training Ayan Chakrabarti Washington University in St. Louis 1 Brookings Dr., St. Louis, MO 63130 ayan@wustl.edu Benjamin Moseley Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA 15213 moseleyb@andrew.cmu.edu Abstract Tra...	2019	266
8,865	Learning to Screen Alon Cohen∗ Avinatan Hassidim† Haim Kaplan‡ Yishay Mansour§ Shay Moran¶ Abstract Imagine a large ﬁrm with multiple departments that plans a large recruitment. Candidates arrive one-by-one, and for each candidate the ﬁrm decides, based on her data (CV, skills, experience, etc), wheth...	2019	267
8,866	A coupled autoencoder approach for multi-modal analysis of cell types Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül Allen Institute, Seattle, WA 98109 rohang@alleninstitute.org, uygars@alleninstitute.org Abstract Recent developm...	2019	268
8,867	Meta-Inverse Reinforcement Learning with Probabilistic Context Variables Lantao Yu∗, Tianhe Yu∗, Chelsea Finn, Stefano Ermon Department of Computer Science, Stanford University Stanford, CA 94305 {lantaoyu,tianheyu,cbfinn,ermon}@cs.stanford.edu Abstract Providing a suitable reward function to reinforcemen...	2019	269
8,868	Hyperbolic Graph Convolutional Neural Networks Ines Chami∗‡ Rex Ying∗† Christopher R´e† Jure Leskovec† †Department of Computer Science, Stanford University ‡Institute for Computational and Mathematical Engineering, Stanford University {chami, rexying, chrismre, jure}@cs.stanford.edu Abstract Graph con...	2019	27
8,869	Precision-Recall Balanced Topic Modelling Seppo Virtanen University of Cambridge sjv35@cam.ac.uk Mark Girolami University of Cambridge and The Alan Turing Institute mag92@cam.ac.uk Abstract Topic models are becoming increasingly relevant probabilistic models for dimensionality reduction of text data, in...	2019	270
8,870	Exact inference in structured prediction Kevin Bello Department of Computer Science Purdue Univeristy West Lafayette, IN 47906, USA kbellome@purdue.edu Jean Honorio Department of Computer Science Purdue Univeristy West Lafayette, IN 47906, USA jhonorio@purdue.edu Abstract Structured prediction c...	2019	271
8,871	Practical and Consistent Estimation of f-Divergences Paul K. Rubenstein⇤ Max Planck Institute for Intelligent Systems, Tübingen & Machine Learning Group, University of Cambridge paul.rubenstein@tuebingen.mpg.de Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin Google Research, Brain Team, Z...	2019	272
8,872	Policy Poisoning in Batch Reinforcement Learning and Control Yuzhe Ma University of Wisconsin–Madison yzm234@cs.wisc.edu Xuezhou Zhang University of Wisconsin–Madison zhangxz1123@cs.wisc.edu Wen Sun Microsoft Research New York Sun.Wen@microsoft.com Xiaojin Zhu University of Wisconsin–Madison j...	2019	273
8,873	R2D2: Repeatable and Reliable Detector and Descriptor Jerome Revaud Philippe Weinzaepfel César De Souza Martin Humenberger NAVER LABS Europe firstname.lastname@naverlabs.com Abstract Interest point detection and local feature description are fundamental steps in many computer vision applications. Clas...	2019	274
8,874	First Order Motion Model for Image Animation Aliaksandr Siarohin DISI, University of Trento aliaksandr.siarohin@unitn.it Stéphane Lathuilière DISI, University of Trento LTCI, Télécom Paris, Institut polytechnique de Paris stephane.lathuilire@telecom-paris.fr Sergey Tulyakov Snap Inc. stulyakov@snap....	2019	275
8,875	Scalable inference of topic evolution via models for latent geometric structures Mikhail Yurochkin IBM Research mikhail.yurochkin@ibm.com Zhiwei Fan University of Wisconsin-Madison zhiwei@cs.wisc.edu Aritra Guha University of Michigan aritra@umich.edu Paraschos Koutris University of Wisconsin-Ma...	2019	276
8,876	Anti-efﬁcient encoding in emergent communication Rahma Chaabouni1,2, Eugene Kharitonov1, Emmanuel Dupoux1,2 and Marco Baroni1,3 1Facebook AI Research 2Cognitive Machine Learning (ENS - EHESS - PSL Research University - CNRS - INRIA) 3ICREA {rchaabouni,kharitonov,dpx,mbaroni}@fb.com Abstract Despite renewe...	2019	277
8,877	Improving Black-box Adversarial Attacks with a Transfer-based Prior Shuyu Cheng∗, Yinpeng Dong∗, Tianyu Pang, Hang Su, Jun Zhu† Dept. of Comp. Sci. and Tech., BNRist Center, State Key Lab for Intell. Tech. & Sys., Institute for AI, THBI Lab, Tsinghua University, Beijing, 100084, China {chengsy18, dyp17, pty17...	2019	278
8,878	REM: From Structural Entropy To Community Structure Deception Yiwei Liu1,2, Jiamou Liu3, Zijian Zhang1,3, Liehuang Zhu1∗, Angsheng Li4 1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China 2Institute of Cyberspace Research, Zhejiang University, Zhejiang 310027, China ...	2019	279
8,879	Spherical Text Embedding Yu Meng1, Jiaxin Huang1, Guangyuan Wang1, Chao Zhang2, Honglei Zhuang1⇤, Lance Kaplan3, Jiawei Han1 1 Department of Computer Science, University of Illinois at Urbana-Champaign 2 College of Computing, Georgia Institute of Technology 3 U.S. Army Research Laboratory 1 {yumeng5,jiaxinh...	2019	28
8,880	Unsupervised Object Segmentation by Redrawing Mickaël Chen Sorbonne Université, CNRS, LIP6, F-75005, Paris, France mickael.chen@lip6.fr Thierry Artières Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France Ecole Centrale Marseille thierry.artieres@centrale-marseille.fr Ludovic Denoyer ...	2019	280
8,881	Unlabeled Data Improves Adversarial Robustness Yair Carmon⇤ Stanford University yairc@stanford.edu Aditi Raghunathan* Stanford University aditir@stanford.edu Ludwig Schmidt UC Berkeley ludwig@berkeley.edu Percy Liang Stanford University pliang@cs.stanford.edu John C. Duchi Stanford Universit...	2019	281
8,882	Optimal Stochastic and Online Learning with Individual Iterates Yunwen Lei1,2 Peng Yang1 Ke Tang1∗Ding-Xuan Zhou3 1University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055...	2019	282
8,883	The Implicit Bias of AdaGrad on Separable Data Qian Qian Department of Statistics Ohio State University Columbus, OH 43210, USA qian.216@osu.edu Xiaoyuan Qian School of Mathematical Sciences Dalian University of Technology Dalian, Liaoning 116024, China xyqian@dlut.edu.cn Abstract We study the i...	2019	283
8,884	iSplit LBI: Individualized Partial Ranking with Ties via Split LBI Qianqian Xu1 Xinwei Sun2 Zhiyong Yang3,4 Xiaochun Cao3,4,7 Qingming Huang1,5,6,7 Yuan Yao8 1Key Lab. of Intelligent Information Processing, Institute of Computing Technology, CAS 2Microsoft Research Asia 3State Key Laboratory of Info...	2019	284
8,885	PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation 1Can Qin∗, 2Haoxuan You∗, 1Lichen Wang, 3C.-C. Jay Kuo, 1,4Yun Fu 1Department of Electrical & Computer Engineering, Northeastern University 2Department of Computer Science, Columbia University 3Department of Electrical and Compu...	2019	285
8,886	Certiﬁed Adversarial Robustness with Additive Noise Bai Li Department of Statistical Science Duke University bai.li@duke.edu Changyou Chen Department of CSE University at Buffalo, SUNY cchangyou@gmail.com Wenlin Wang Department of ECE Duke University wenlin.wang@duke.edu Lawrence Carin Dep...	2019	286
8,887	Self-Critical Reasoning for Robust Visual Question Answering Jialin Wu Department of Computer Science University of Texas at Austin jialinwu@utexas.edu Raymond J. Mooney Department of Computer Science University of Texas at Austin mooney@cs.utexas.edu Abstract Visual Question Answering (VQA) deep-...	2019	287
8,888	Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer Arsenii Vanunts Yandex Moscow, Russia avanunts@yandex.ru Alexey Drutsa Yandex; MSU Moscow, Russia adrutsa@yandex.ru Abstract We study revenue optimization pricing algorithms for repeated posted-pr...	2019	288
8,889	Stand-Alone Self-Attention in Vision Models Prajit Ramachandran∗ Niki Parmar∗ Ashish Vaswani∗ Irwan Bello Anselm Levskaya† Jonathon Shlens Google Research, Brain Team {prajit, nikip, avaswani}@google.com Abstract Convolutions are a fundamental building block of modern computer vision systems. Rece...	2019	289
8,890	Random Tessellation Forests Shufei Ge1 shufei_ge@sfu.ca Shijia Wang2,1 shijia_wang@sfu.ca Yee Whye Teh3 y.w.teh@stats.ox.ac.uk Liangliang Wang1 liangliang_wang@sfu.ca Lloyd T. Elliott1 lloyd_elliott@sfu.ca 1Department of Statistics and Actuarial Science, Simon Fraser University, Canada 2School o...	2019	29
8,891	Debiased Bayesian inference for average treatment effects Kolyan Ray Department of Mathematics King’s College London kolyan.ray@kcl.ac.uk Botond Szabó Mathematical Institute Leiden University b.t.szabo@math.leidenuniv.nl Abstract Bayesian approaches have become increasingly popular in causal infer...	2019	290
8,892	Globally optimal score-based learning of directed acyclic graphs in high-dimensions Bryon Aragam1 Arash A. Amini2 Qing Zhou2 1University of Chicago bryon@chicagobooth.edu 2University of California, Los Angeles {aaamini,zhou}@stat.ucla.edu Abstract We prove that ⌦(s log p) samples sufﬁce to learn a s...	2019	291
8,893	GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs Yuan Liu Zehong Shen Zhixuan Lin Sida Peng Hujun Bao∗ Xiaowei Zhou∗ State Key Lab of CAD&CG, ZJU-Sensetime Joint Lab of 3D Vision, Zhejiang University Abstract Finding local correspondences between images with different viewpoin...	2019	292
8,894	Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao1,∗Tianle Cai1,∗Haochuan Li2 Liwei Wang3 Cho-Jui Hsieh4 Jason D. Lee5 1School of Mathematical Sciences, Peking University 2Department of EECS, Massachusetts Institute of Technology 3Key Laboratory of Machine Perception, MOE...	2019	293
8,895	Explicit Disentanglement of Appearance and Perspective in Generative Models Nicki S. Detlefsen ∗ nsde@dtu.dk Søren Hauberg ∗ sohau@dtu.dk Abstract Disentangled representation learning ﬁnds compact, independent and easy-tointerpret factors of the data. Learning such has been shown to require an inductive ...	2019	294
8,896	Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes James P. Bailey Texas A&M University jamespbailey@tamu.edu Georgios Piliouras Singapore University of Technology and Design georgios@sutd.edu.sg Abstract We show for the ﬁrst time that it is possible to reconci...	2019	295
8,897	Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré Stanford University vincentsc@cs.stanford.edu, senwu@stanford.edu, zzweng@stanford.edu, ajratner@stanford.edu, chrismre@cs.stanford.edu Abstract ...	2019	296
8,898	Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin∗ Ilias Diakonikolas University of Wisconsin-Madison ilias@cs.wisc.edu Daniel M. Kane University of California, San Diego dakane@cs.ucsd.edu Pasin Manurangsi† University of California, Berkeley pasin@berkeley.edu Abstract ...	2019	297
8,899	Distribution-Independent PAC Learning of Halfspaces with Massart Noise Ilias Diakonikolas University of Wisconsin-Madison ilias@cs.wisc.edu Themis Gouleakis Max Planck Institute for Informatics tgouleak@mpi-inf.mpg.de Christos Tzamos University of Wisconsin-Madison tzamos@wisc.edu Abstract We st...	2019	298

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