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9,000 | General E(2) - Equivariant Steerable CNNs Maurice Weiler∗ University of Amsterdam, QUVA Lab m.weiler@uva.nl Gabriele Cesa∗† University of Amsterdam cesa.gabriele@gmail.com Abstract The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of eq... | 2019 | 389 |
9,001 | Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals Surbhi Goel Department of Computer Science University of Texas at Austin surbhi@cs.utexas.edu Sushrut Karmalkar Department of Computer Science University of Texas at Austin sushrutk@cs.utexas.edu Adam R. Klivans Departm... | 2019 | 39 |
9,002 | On the Convergence of Single-Call Stochastic Extra-Gradient Methods Yu-Guan Hsieh Univ. Grenoble Alpes, LJK and ENS Paris 38000 Grenoble, France. yu-guan.hsieh@ens.fr Franck Iutzeler Univ. Grenoble Alpes, LJK 38000 Grenoble, France. franck.iutzeler@univ-grenoble-alpes.fr Jérôme Malick CNRS, LJK ... | 2019 | 390 |
9,003 | Learning nonlinear level sets for dimensionality reduction in function approximation Guannan Zhang Computer Science and Mathematics Division Oak Ridge National Laboratory zhangg@ornl.gov Jiaxin Zhang National Center for Computational Sciences Oak Ridge National Laboratory zhangj@ornl.gov Jacob Hinkl... | 2019 | 391 |
9,004 | Regularized Gradient Boosting Corinna Cortes Google Research New York, NY 10011 corinna@google.com Mehryar Mohri Google & Courant Institute New York, NY 10012 mohri@google.com Dmitry Storcheus Courant Institute & Google New York, NY 10012 dstorcheus@google.com Abstract Gradient Boosting (GB)... | 2019 | 392 |
9,005 | Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models Vincent Le Guen 1,2 vincent.le-guen@edf.fr Nicolas Thome 2 nicolas.thome@cnam.fr (1) EDF R&D 6 quai Watier, 78401 Chatou, France (2) CEDRIC, Conservatoire National des Arts et Métiers 292 rue Saint-Martin, 75003 Paris, Fra... | 2019 | 393 |
9,006 | General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme∗ Tao Sun College of Computer National University of Defense Technology Changsha, Hunan 410073, China nudtsuntao@163.com Yuejiao Sun Department of Mathematics University of California, Los Angel... | 2019 | 394 |
9,007 | Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets Rohith Kuditipudi Duke University rohith.kuditipudi@duke.edu Xiang Wang Duke University xwang@cs.duke.edu Holden Lee Princeton University holdenl@princeton.edu Yi Zhang Princeton University y.zhang@cs.princeton.edu Z... | 2019 | 395 |
9,008 | Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner1,2,3 kunstner@cs.ubc.ca Lukas Balles2,3 lballes@tue.mpg.de Philipp Hennig2,3 ph@tue.mpg.de École Polytechnique Fédérale de Lausanne (EPFL), Switzerland1 University of Tübingen, Germany2 Max Planck Instit... | 2019 | 396 |
9,009 | Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression ⇤ Deeksha Adil Department of Computer Science University of Toronto deeksha@cs.toronto.edu Richard Peng School of Computer Science Georgia Institute of Technology rpeng@cc.gatech.edu Sushant Sachdeva Department of Computer Scien... | 2019 | 397 |
9,010 | A Model to Search for Synthesizable Molecules John Bradshaw University of Cambridge MPI for Intelligent Systems jab255@cam.ac.uk Brooks Paige University of Cambridge The Alan Turing Institute bpaige@turing.ac.uk Matt J. Kusner University College London The Alan Turing Institute m.kusner@ucl.ac.u... | 2019 | 398 |
9,011 | Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar∗, Xiao Zhang∗, Mohammad Mahmoody, and David Evans University of Virginia [saeed, shawn, mohammad, evans]@virginia.edu Abstract Many recent works have shown that adversarial examples that fool classifiers can be ... | 2019 | 399 |
9,012 | Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck The Alan Turing Institute Department of Computer Science University of Warwick ohamelijnck@turing.ac.uk Theodoros Damoulas The Alan Turing Institute Depts. of Computer Science & Statistics University of Warwick tdamoulas@turing.ac.uk ... | 2019 | 4 |
9,013 | Bayesian Optimization under Heavy-tailed Payoffs Sayak Ray Chowdhury Department of ECE Indian Institute of Science Bangalore, India 560012 sayak@iisc.ac.in Aditya Gopalan Department of ECE Indian Institute of Science Bangalore, India 560012 aditya@iisc.ac.in Abstract We consider black box optimi... | 2019 | 40 |
9,014 | Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries Fuwen Tan University of Virginia fuwen.tan@virginia.edu Paola Cascante-Bonilla University of Virginia pc9za@virginia.com Xiaoxiao Guo IBM Research AI xiaoxiao.guo@ibm.com Hui Wu IBM Research AI wuhu@us.ibm.com ... | 2019 | 400 |
9,015 | Provably Efficient Q-Learning with Low Switching Cost Yu Bai Stanford University yub@stanford.edu Tengyang Xie Nan Jiang UIUC {tx10, nanjiang}@illinois.edu Yu-Xiang Wang UC Santa Barbara yuxiangw@cs.ucsb.edu Abstract We take initial steps in studying PAC-MDP algorithms with limited adaptivity, ... | 2019 | 401 |
9,016 | Fast and Accurate Least-Mean-Squares Solvers Alaa Maalouf ∗ Alaamalouf12@gmail.com Ibrahim Jubran∗ ibrahim.jub@gmail.com Dan Feldman dannyf.post@gmail.com The Robotics and Big Data Lab, Department of Computer Science, University of Haifa, Haifa, Israel Abstract Least-mean squares (LMS) solvers s... | 2019 | 402 |
9,017 | Graph Agreement Models for Semi-Supervised Learning Otilia Stretcu‡∗, Krishnamurthy Viswanathan†, Dana Movshovitz-Attias†, Emmanouil Antonios Platanios‡, Andrew Tomkins†, Sujith Ravi† †Google Research, ‡Carnegie Mellon University ostretcu@cs.cmu.edu,{kvis,danama}@google.com, e.a.platanios@cs.cmu.edu,{tomkin... | 2019 | 403 |
9,018 | Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection Bingzhe Wu1, Shiwan Zhao2, ChaoChao Chen3, Haoyang Xu1 Li Wang3, Xiaolu Zhang3, Guangyu Sun1,4∗, Jun Zhou3 1Peking University, 2IBM Research, 3Ant Financial, 4 Advanced Institute of Information Technology, Peking Un... | 2019 | 404 |
9,019 | Large Scale Structure of Neural Network Loss Landscapes Stanislav Fort∗ Google Research Zurich, Switzerland Stanislaw Jastrzebski† New York University New York, United States Abstract There are many surprising and perhaps counter-intuitive properties of optimization of deep neural networks. We propo... | 2019 | 405 |
9,020 | Model Similarity Mitigates Test Set Overuse Horia Mania UC Berkeley hmania@berkeley.edu John Miller UC Berkeley miller_john@berkeley.edu Ludwig Schmidt UC Berkeley ludwig@berkeley.edu Moritz Hardt UC Berkeley hardt@berkeley.edu Benjamin Recht UC Berkeley brecht@berkeley.edu Abstract Ex... | 2019 | 406 |
9,021 | Explicit Planning for Efficient Exploration in Reinforcement Learning Liangpeng Zhang1, Ke Tang2, and Xin Yao2,1∗ 1CERCIA, School of Computer Science, University of Birmingham, U.K. 2Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Pr... | 2019 | 407 |
9,022 | vGraph: A Generative Model for Joint Community Detection and Node Representation Learning Fan-Yun Sun1,2, Meng Qu2, Jordan Hoffmann2,3, Chin-Wei Huang2,4, Jian Tang2,5,6 1National Taiwan University, 2Mila-Quebec Institute for Learning Algorithms, Canada 3Harvard University, USA 4Element AI, Canada 5HEC Mo... | 2019 | 408 |
9,023 | Can Unconditional Language Models Recover Arbitrary Sentences? Nishant Subramani New York University nishant@nyu.edu Samuel R. Bowman New York University Kyunghyun Cho New York Univeristy Facebook AI Research CIFAR Azrieli Global Scholar Abstract Neural network-based generative language models l... | 2019 | 409 |
9,024 | Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor Meera Pai and Animesh Kumar Electrical Engineering Indian Institute of Technology Bombay Mumbai 400076 India meeravpai,animesh@ee.iitb.ac.in Abstract Measurement of spatial fields is of interest in environment monitorin... | 2019 | 41 |
9,025 | A Kernel Loss for Solving the Bellman Equation Yihao Feng UT Austin yihao@cs.utexas.edu Lihong Li Google Research lihong@google.com Qiang Liu UT Austin lqiang@cs.utexas.edu Abstract Value function learning plays a central role in many state-of-the-art reinforcementlearning algorithms. Many popul... | 2019 | 410 |
9,026 | Covariate-Powered Empirical Bayes Estimation Nikolaos Ignatiadis Statistics Department Stanford University ignat@stanford.edu Stefan Wager Graduate School of Business Stanford University swager@stanford.edu Abstract We study methods for simultaneous analysis of many noisy experiments in the presen... | 2019 | 411 |
9,027 | Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks Yuan Cao Department of Computer Science University of California, Los Angeles CA 90095, USA yuancao@cs.ucla.edu Quanquan Gu Department of Computer Science University of California, Los Angeles CA 90095, USA qgu@cs.u... | 2019 | 412 |
9,028 | Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems Yi Xu1, Rong Jin2, Tianbao Yang1 1. Department of Computer Science, The University of Iowa, Iowa City, IA 52246, USA 2. Machine Intelligence Technology, Alibaba Group, Bellevue, WA 98004, USA {yi-xu, tianbao-yang}@uio... | 2019 | 413 |
9,029 | AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling Bichuan Guo Tsinghua University gbc16@mails.tsinghua.edu.cn Yuxing Han South China Agricultural University yuxinghan@scau.edu.cn Jiangtao Wen Tsinghua University jtwen@tsinghua.edu.cn Abstract In this paper we propose to use a de... | 2019 | 414 |
9,030 | Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou1, Shangqing Liu1, ∗, Jingkai Siow1, Xiaoning Du1, ∗, and Yang Liu1 1Nanyang Technological University 1{yqzhou, shangqin001, jingkai001, xiaoning.du, yangliu}@ntu.edu.sg *Co-correspo... | 2019 | 415 |
9,031 | Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht HCI/IWR at Heidelberg University, 69115 Heidelberg, Germany {fita@stud, sebastian.damrich@iwr, fred.hamprecht@iwr}.uni-heidelberg.de Ab... | 2019 | 416 |
9,032 | Learning Robust Options by Conditional Value at Risk Optimization Takuya Hiraoka 1,2,3, Takahisa Imagawa 2, Tatsuya Mori 1,2,3, Takashi Onishi 1,2, Yoshimasa Tsuruoka 2,4 1NEC Corporation 2National Institute of Advanced Industrial Science and Technology 3RIKEN Center for Advanced Intelligence Project 4The... | 2019 | 417 |
9,033 | A Generic Acceleration Framework for Stochastic Composite Optimization Andrei Kulunchakov and Julien Mairal Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France ❛♥❞r❡✐✳❦✉❧✉♥❝❤❛❦♦✈❅✐♥r✐❛✳❢r ❛♥❞❥✉❧✐❡♥✳♠❛✐r❛❧❅✐♥r✐❛✳❢r Abstract In this paper, we introduce various mechanisms to obtain acc... | 2019 | 418 |
9,034 | A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation Runzhe Yang Department of Computer Science Princeton University runzhey@cs.princeton.edu Xingyuan Sun Department of Computer Science Princeton University xs5@cs.princeton.edu Karthik Narasimhan Department of C... | 2019 | 419 |
9,035 | State Aggregation Learning from Markov Transition Data Yaqi Duan Princeton University yaqid@princeton.edu Zheng Tracy Ke Harvard University zke@fas.harvard.edu Mengdi Wang Princeton University mengdiw@princeton.edu Abstract State aggregation is a popular model reduction method rooted in optimal ... | 2019 | 42 |
9,036 | Communication trade-offs for Local-SGD with large step size Kumar Kshitij PATEL MLO, EPFL, Lausanne, Switzerland TTIC-Toyota Technological Institute Chicago kkpatel@ttic.edu Aymeric DIEULEVEUT MLO, EPFL, Lausanne, Switzerland CMAP, Ecole Polytechnique, Palaiseau, France aymeric.dieuleveut@polytechniqu... | 2019 | 420 |
9,037 | Towards modular and programmable architecture search Renato Negrinho1 ∗ Darshan Patil1 Nghia Le1 Daniel Ferreira2 Matthew R. Gormley1 Geoffrey Gordon1,3 Carnegie Mellon University1, TU Wien2, Microsoft Research Montreal3 Abstract Neural architecture search methods are able to find high performance de... | 2019 | 421 |
9,038 | Large-scale optimal transport map estimation using projection pursuit Cheng Meng1 Yuan Ke1 Jingyi Zhang1 Mengrui Zhang1 Wenxuan Zhong1 Ping Ma1 1Department of Statistics, University of Georgia {cheng.meng25, yuan.ke, jingyi.zhang25, mengrui.zhang, wenxuan, pingma }@uga.edu Abstract This paper studies the es... | 2019 | 422 |
9,039 | Understanding Attention and Generalization in Graph Neural Networks Boris Knyazev University of Guelph Vector Institute bknyazev@uoguelph.ca Graham W. Taylor University of Guelph Vector Institute, Canada CIFAR AI Chair gwtaylor@uoguelph.ca Mohamed R. Amer∗ Robust.AI mohamed@robust.ai Abstract ... | 2019 | 423 |
9,040 | Superposition of many models into one Brian Cheung Redwood Center, BAIR UC Berkeley bcheung@berkeley.edu Alex Terekhov Redwood Center UC Berkeley aterekhov@berkeley.edu Yubei Chen Redwood Center, BAIR UC Berkeley yubeic@berkeley.edu Pulkit Agrawal BAIR UC Berkeley pulkitag@berkeley.edu ... | 2019 | 424 |
9,041 | A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models Maksim Kuznetsov1,∗ Daniil Polykovskiy1,∗ Dmitry Vetrov2 Alexander Zhebrak1 1Insilico Medicine 2National Research University Higher School of Economics {kuznetsov,daniil,zhebrak}@insilico.com vetrovd@yandex.ru Abstract ... | 2019 | 425 |
9,042 | Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Mücke Institute for Stochastics and Applications University of Stuttgart nicole.muecke@mathematik.uni-stuttgart.de Gergely Neu Universitat Pompeu Fabra gergely.neu@gmail.com Lorenzo Rosasco Universita’ degli Studi di Genova Istitut... | 2019 | 426 |
9,043 | Extending Stein’s unbiased risk estimator to train deep denoisers with correlated pairs of noisy images Magauiya Zhussip Shakarim Soltanayev Se Young Chun Ulsan National Institute of Science and Technology (UNIST) {mzhussip, shakarim, sychun}@unist.ac.kr Abstract Recently, Stein’s unbiased risk estimato... | 2019 | 427 |
9,044 | Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models Farnam Mansouri† Yuxin Chen‡ Ara Vartanian‹ Xiaojin Zhu‹ Adish Singla† †Max Planck Institute for Software Systems (MPI-SWS), {mfarnam, adishs}@mpi-sws.org, ‡University of Chicago, chenyuxin@uchicago.edu, ‹University of Wis... | 2019 | 428 |
9,045 | Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm Amir-massoud Farahmand∗ Vector Institute & University of Toronto Toronto, Canada farahmand@vectorinstitute.ai Abstract This paper considers the problem of estimating the distribution of returns in reinforcement learning, i... | 2019 | 429 |
9,046 | Reliable training and estimation of variance networks Nicki S. Detlefsen∗† nsde@dtu.dk Martin Jørgensen* † marjor@dtu.dk Søren Hauberg † sohau@dtu.dk Abstract We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks. We derive... | 2019 | 43 |
9,047 | Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients Jun Sun† Zhejiang University Hangzhou, China 310027 sunjun16sj@gmail.com Tianyi Chen† Rensselaer Polytechnic Institute Troy, New York 12180 chent18@rpi.edu Georgios B. Giannakis University of Minnesota, Twin Citi... | 2019 | 430 |
9,048 | Twin Auxiliary Classifiers GAN Mingming Gong *1,3, Yanwu Xu *1, Chunyuan Li2, Kun Zhang3, and Kayhan Batmanghelich1 1Department of Biomedical Informatics, University of Pittsburgh, {mig73,yanwuxu,kayhan}@pitt.edu 2Microsoft Research, Redmond, cl319@duke.edu 3Department of Philosophy, Carnegie Mellon University, ... | 2019 | 431 |
9,049 | Online Prediction of Switching Graph Labelings with Cluster Specialists Mark Herbster Department of Computer Science University College London London United Kingdom m.herbster@cs.ucl.ac.uk James Robinson Department of Computer Science University College London London United Kingdom j.robinson@... | 2019 | 432 |
9,050 | AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters Xia Xiao, Zigeng Wang, Sanguthevar Rajasekaran∗ Department of Computer Science and Engineering University of Connecticut Storrs, CT, USA, 06269 {xia.xiao, zigeng.wang, sanguthevar.rajasekaran}@uconn.edu Abstract Reducing the model... | 2019 | 433 |
9,051 | Understanding the Role of Momentum in Stochastic Gradient Methods Igor Gitman Hunter Lang Pengchuan Zhang Lin Xiao Microsoft Research AI Redmond, WA 98052, USA {igor.gitman, hunter.lang, penzhan, lin.xiao}@microsoft.com Abstract The use of momentum in stochastic gradient methods has become a widespr... | 2019 | 434 |
9,052 | DAC: The Double Actor-Critic Architecture for Learning Options Shangtong Zhang, Shimon Whiteson Department of Computer Science University of Oxford {shangtong.zhang, shimon.whiteson}@cs.ox.ac.uk Abstract We reformulate the option framework as two parallel augmented MDPs. Under this novel formulation, al... | 2019 | 435 |
9,053 | Safe Exploration for Interactive Machine Learning Matteo Turchetta Dept. of Computer Science ETH Zurich matteotu@inf.ethz.ch Felix Berkenkamp Dept. of Computer Science ETH Zurich befelix@inf.ethz.ch Andreas Krause Dept. of Computer Science ETH Zurich krausea@ethz.ch Abstract In Interactive M... | 2019 | 436 |
9,054 | Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning Akihiro Kishimoto IBM Research, Ireland Beat Buesser IBM Research, Ireland Bei Chen IBM Research, Ireland Adi Botea Eaton, Ireland∗ Abstract Search techniques, such as Monte Carlo Tree Search ... | 2019 | 437 |
9,055 | Learning from Label Proportions with Generative Adversarial Networks Jiabin Liu∗ Samsung Research China - Beijing Beijing 100028, China liujiabin008@126.com Bo Wang∗ University of International Business and Economics Beijing 100029, China wangbo@uibe.edu.cn Zhiquan Qi† Yingjie Tian Yong Shi Un... | 2019 | 438 |
9,056 | Sparse High-Dimensional Isotonic Regression David Gamarnik ∗ Sloan School of Management Massachusetts Institute of Technology Cambridge, MA 02139 gamarnik@mit.edu Julia Gaudio† Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139 jgaudio@mit.edu Abstract We consid... | 2019 | 439 |
9,057 | Meta-Learning with Implicit Gradients Aravind Rajeswaran∗ University of Washington aravraj@cs.washington.edu Chelsea Finn∗ University of California Berkeley cbfinn@cs.stanford.edu Sham M. Kakade University of Washington sham@cs.washington.edu Sergey Levine University of California Berkeley svlev... | 2019 | 44 |
9,058 | Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation Ruibo Tu KTH Royal Institute of Technology ruibo@kth.se Kun Zhang Carnegie Mellon University kunz1@cmu.edu Bo Christer Bertilson Karolinska Institute bo.bertilson@ki.se Hedvig Kjellström KTH Royal Institute of Technol... | 2019 | 440 |
9,059 | Budgeted Reinforcement Learning in Continuous State Space Nicolas Carrara∗ SequeL team, INRIA Lille – Nord Europe† nicolas.carrara@inria.fr Edouard Leurent∗ SequeL team, INRIA Lille – Nord Europe† Renault Group, France edouard.leurent@inria.fr Romain Laroche Microsoft Research, Montreal, Canada ro... | 2019 | 441 |
9,060 | Parameter elimination in particle Gibbs sampling Anna Wigren Department of Information Technology Uppsala University, Sweden anna.wigren@it.uu.se Riccardo Sven Risuleo Department of Information Technology Uppsala University, Sweden riccardo.risuleo@it.uu.se Lawrence Murray Uber AI San Francisco, C... | 2019 | 442 |
9,061 | Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling Tengyang Xie∗ Dept. of Computer Science UIUC Urbana, IL 61801 tx10@illinois.edu Yifei Ma AWS AI Labs Amazon.com Services, Inc. East Palo Alto, CA 94303 yifeim@amazon.com Yu-Xiang Wang Dept.... | 2019 | 443 |
9,062 | Understanding Sparse JL for Feature Hashing Meena Jagadeesan∗ Harvard University Cambridge, MA 02138 mjagadeesan@college.harvard.edu Abstract Feature hashing and other random projection schemes are commonly used to reduce the dimensionality of feature vectors. The goal is to efficiently project a high-di... | 2019 | 444 |
9,063 | Planning in entropy-regularized Markov decision processes and games Jean-Bastien Grill∗ DeepMind Paris jbgrill@google.com Omar D. Domingues∗ SequeL team, Inria Lille omar.darwiche-domingues@inria.fr Pierre Ménard SequeL team, Inria Lille pierre.menard@inria.fr Rémi Munos DeepMind Paris munos@g... | 2019 | 445 |
9,064 | Dynamic Local Regret for Non-convex Online Forecasting Sergul Aydore ∗ Department of ECE Stevens Institute of Technology Hoboken, NJ USA sergulaydore@gmail.com Tianhao Zhu Department of ECE Stevens Institute of Technology Hoboken, NJ USA romeo.zhuth@gmail.com Dean Foster Amazon New York, NY ... | 2019 | 446 |
9,065 | NAOMI: Non-Autoregressive Multiresolution Sequence Imputation Yukai Liu Caltech yukai@caltech.edu Rose Yu Northeastern University roseyu@northeastern.edu Stephan Zheng∗ Caltech, Salesforce stephan.zheng@salesforce.com Eric Zhan Caltech ezhan@caltech.edu Yisong Yue Caltech yyue@caltech.ed... | 2019 | 447 |
9,066 | Write, Execute, Assess: Program Synthesis with a REPL Kevin Ellis∗ MIT Maxwell Nye∗ MIT Yewen Pu∗ MIT Felix Sosa∗ Harvard University Joshua B. Tenenbaum MIT Armando Solar-Lezama MIT Abstract We present a neural program synthesis approach integrating components which write, execute, and a... | 2019 | 448 |
9,067 | Conformalized Quantile Regression Yaniv Romano Department of Statistics Stanford University Evan Patterson Department of Statistics Stanford University Emmanuel J. Candès Departments of Mathematics and of Statistics Stanford University Abstract Conformal prediction is a technique for constructing ... | 2019 | 449 |
9,068 | Differentially Private Markov Chain Monte Carlo Mikko A. Heikkilä ∗ Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics University of Helsinki, Helsinki, Finland mikko.a.heikkila@helsinki.fi Joonas Jälkö ∗ Helsinki Institute for Information Technology HIIT, Departmen... | 2019 | 45 |
9,069 | Multiagent Evaluation under Incomplete Information Mark Rowland1,∗ markrowland@google.com Shayegan Omidshafiei2,∗ somidshafiei@google.com Karl Tuyls2 karltuyls@google.com Julien Pérolat1 perolat@google.com Michal Valko2 valkom@deepmind.com Georgios Piliouras3 georgios@sutd.edu.sg Rémi Munos2 ... | 2019 | 450 |
9,070 | SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms Zhe Wang Department of ECE The Ohio State University wang.10982@osu.edu Kaiyi Ji Department of ECE The Ohio State University ji.367@osu.edu Yi Zhou Department of ECE The University of Utah yi.zhou@utah.edu Yingbin Lian... | 2019 | 451 |
9,071 | Mixtape: Breaking the Softmax Bottleneck Efficiently Zhilin Yang1, Thang Luong2, Ruslan Salakhutdinov1, Quoc Le2 1Carnegie Mellon University, 2Google Brain {zhiliny,rsalakhu}@cs.cmu.edu, {thangluong,qvl}@google.com Abstract The softmax bottleneck has been shown to limit the expressiveness of neural language mo... | 2019 | 452 |
9,072 | High-Dimensional Optimization in Adaptive Random Subspaces Jonathan Lacotte Department of Electrical Engineering Stanford University lacotte@stanford.edu Mert Pilanci Department of Electrical Engineering Stanford University Marco Pavone Department of Aeronautics &Astronautics Stanford University ... | 2019 | 453 |
9,073 | Flexible information routing in neural populations through stochastic comodulation Caroline Haimerl Center for Neural Science New York University ch2880@nyu.edu Cristina Savin Center for Neural Science Center for Data Science New York University csavin@nyu.edu Eero P. Simoncelli Center for Neura... | 2019 | 454 |
9,074 | MarginGAN: Adversarial Training in Semi-Supervised Learning Jinhao Dong School of Computer Science and Technology, Xidian University Xi’an 710126, China jhdong@stu.xidian.edu.cn Tong Lin∗ Key Laboratory of Machine Perception, MOE School of EECS, Peking University, Beijing, & Peng Cheng Laboratory, S... | 2019 | 455 |
9,075 | Cold Case: the Lost MNIST Digits Chhavi Yadav New York University New York, NY chhavi@nyu.edu Léon Bottou Facebook AI Research and New York University New York, NY leon@bottou.org Abstract Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hana... | 2019 | 456 |
9,076 | RUBi: Reducing Unimodal Biases for Visual Question Answering Remi Cadene 1⇤, Corentin Dancette 1⇤, Hedi Ben-younes 1, Matthieu Cord 1, Devi Parikh 2,3 1 Sorbonne Université, CNRS, LIP6, 4 place Jussieu, 75005 Paris, 2 Facebook AI Research, 3 Georgia Institute of Technology {remi.cadene, corentin.dancette, hed... | 2019 | 457 |
9,077 | Reward Constrained Interactive Recommendation with Natural Language Feedback Ruiyi Zhang1∗, Tong Yu2∗, Yilin Shen2, Hongxia Jin2, Changyou Chen3, Lawrence Carin1 1 Duke University, 2 Samsung Research America, 3 University at Buffalo Abstract Text-based interactive recommendation provides richer user feedback ... | 2019 | 458 |
9,078 | Learning to Correlate in Multi-Player General-Sum Sequential Games Andrea Celli∗ Politecnico di Milano andrea.celli@polimi.it Alberto Marchesi∗ Politecnico di Milano alberto.marchesi@polimi.it Tommaso Bianchi Politecnico di Milano tommaso4.bianchi@mail.polimi.it Nicola Gatti Politecnico di Milan... | 2019 | 459 |
9,079 | Universal Boosting Variational Inference Trevor Campbell Department of Statistics University of British Columbia Vancouver, BC V6T 1Z4 trevor@stat.ubc.ca Xinglong Li Department of Statistics University of British Columbia Vancouver, BC V6T 1Z4 xinglong.li@stat.ubc.ca Abstract Boosting variationa... | 2019 | 46 |
9,080 | Learning Sample-Specific Models with Low-Rank Personalized Regression Benjamin Lengerich Carnegie Mellon University blengeri@cs.cmu.edu Bryon Aragam University of Chicago bryon@chicagobooth.edu Eric P. Xing Carnegie Mellon University epxing@cs.cmu.edu Abstract Modern applications of machine learn... | 2019 | 460 |
9,081 | Learning Reward Machines for Partially Observable Reinforcement Learning Rodrigo Toro Icarte∗ University of Toronto Vector Institute Ethan Waldie University of Toronto Toryn Q. Klassen University of Toronto Vector Institute Richard Valenzano Element AI Margarita P. Castro University of Toronto... | 2019 | 461 |
9,082 | Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs Himanshu Sahni†∗ Toby Buckley‡ Pieter Abbeel‡, § Ilya Kuzovkin‡ † Georgia Institute of Technology ‡ OffWorld Inc. § University of California, Berkeley Abstract Reinforcement Learning (RL) algorithms typically require milli... | 2019 | 462 |
9,083 | Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes Gunpil Hwang∗, Seohyeon Kim∗, and Hyeon-Min Bae School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon, South Korea {gphwang, dddokman, hmbae}@kaist.ac.kr Abstract In this paper, ... | 2019 | 463 |
9,084 | Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration Robert Kleinberg Department of Computer Science Cornell University rdk@cs.cornell.edu Kevin Leyton-Brown Department of Computer Science University of British Columbia kevinlb@cs.ubc.ca Brendan Lucier Microsoft R... | 2019 | 464 |
9,085 | Unsupervised Scalable Representation Learning for Multivariate Time Series Jean-Yves Franceschi∗ Sorbonne Université, CNRS, LIP6, F-75005 Paris, France jean-yves.franceschi@lip6.fr Aymeric Dieuleveut MLO, EPFL, Lausanne CH-1015, Switzerland CMAP, Ecole Polytechnique, Palaiseau, France aymeric.dieuleveut... | 2019 | 465 |
9,086 | Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels Natalia Neverova, David Novotny, Andrea Vedaldi Facebook AI Research {nneverova, dnovotny, vedaldi}@fb.com Abstract Many machine learning methods depend on human supervision to achieve optimal performance. However, in tasks such a... | 2019 | 466 |
9,087 | Total Least Squares Regression in Input Sparsity Time Huaian Diao Northeast Normal University & KLAS of MOE hadiao@nenu.edu.cn Zhao Song University of Washington zhaosong@uw.edu David P. Woodruff Carnegie Mellon University dwoodruf@cs.cmu.edu Xin Yang University of Washington yx1992@cs.washing... | 2019 | 467 |
9,088 | Bayesian Learning of Sum-Product Networks Martin Trapp1,2, Robert Peharz3, Hong Ge3, Franz Pernkopf1, Zoubin Ghahramani4,3 1Graz University of Technology, 2OFAI, 3University of Cambridge, 4Uber AI martin.trapp@tugraz.at, rp587@cam.ac.uk, hg344@cam.ac.uk pernkopf@tugraz.at, zoubin@eng.cam.ac.uk Abstract ... | 2019 | 468 |
9,089 | DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision Duc Tam Nguyen ∗†‡, Maximilian Dax ∗‡, Chaithanya Kumar Mummadi †§ Thi Phuong Nhung Ngo §, Thi Hoai Phuong Nguyen ¶, Zhongyu Lou ‡, Thomas Brox † Abstract Deep neural network (DNN) based salient object detection in images based on hi... | 2019 | 469 |
9,090 | LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning Yali Du∗ University College London London, UK yali.du@ucl.ac.uk Lei Han∗ Tencent AI Lab Shenzhen, Guangdong, China leihan.cs@gmail.com Meng Fang Tencent Robotics X Shenzhen, Guangdong, China mfang@tencent.com Ti... | 2019 | 47 |
9,091 | Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games Kaiqing Zhang ECE and CSL University of Illinois at Urbana-Champaign kzhang66@illinois.edu Zhuoran Yang ORFE Princeton University zy6@princeton.edu Tamer Ba¸sar ECE and CSL University of Illinois at Urba... | 2019 | 470 |
9,092 | On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir Weizmann Institute of Science {gilad.yehudai,ohad.shamir}@weizmann.ac.il Abstract Recently, a spate of papers have provided positive theoretical results for training over-parameterized neural netw... | 2019 | 471 |
9,093 | Real-Time Reinforcement Learning Simon Ramstedt Mila, Element AI, Université de Montréal simonramstedt@gmail.com Christopher Pal Mila, Element AI, Polytechnique Montréal christopher.pal@polymtl.ca Abstract Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in R... | 2019 | 472 |
9,094 | Discriminative Topic Modeling with Logistic LDA Iryna Korshunova Ghent University iryna.korshunova@ugent.be Hanchen Xiong Twitter hxiong@twitter.com Mateusz Fedoryszak Twitter mfedoryszak@twitter.com Lucas Theis Twitter ltheis@twitter.com Abstract Despite many years of research into latent D... | 2019 | 473 |
9,095 | Streaming Bayesian Inference for Crowdsourced Classification Edoardo Manino University of Southampton E.Manino@soton.ac.uk Long Tran-Thanh University of Southampton l.tran-thanh@soton.ac.uk Nicholas R. Jennings Imperial College, London n.jennings@imperial.ac.uk Abstract A key challenge in crowdso... | 2019 | 474 |
9,096 | Disentangling Influence: Using disentangled representations to audit model predictions∗ Charles T. Marx Haverford College cmarx@haverford.edu Richard Lanas Phillips Cornell University rlp246@cornell.edu Sorelle A. Friedler Haverford College sorelle@cs.haverford.edu Carlos Scheidegger University o... | 2019 | 475 |
9,097 | Deep Structured Prediction for Facial Landmark Detection Lisha Chen1, Hui Su1,2, Qiang Ji1 1Rensselaer Polytechnic Institute, 2IBM Research chenl21@rpi.edu, huisuibmres@us.ibm.com, jiq@rpi.edu Abstract Existing deep learning based facial landmark detection methods have achieved excellent performance. Thes... | 2019 | 476 |
9,098 | Mutually Regressive Point Processes Ifigeneia Apostolopoulou Machine Learning Department Carnegie Mellon University iapostol@andrew.cmu.edu Scott Linderman Department of Statistics Stanford University scott.linderman@stanford.edu Kyle Miller AutonLab Carnegie Mellon University mille856@andrew.cmu... | 2019 | 477 |
9,099 | Demystifying Black-box Models with Symbolic Metamodels Ahmed M. Alaa ECE Department UCLA ahmedmalaa@ucla.edu Mihaela van der Schaar UCLA, University of Cambridge, and Alan Turing Institute {mv472@cam.ac.uk,mihaela@ee.ucla.edu} Abstract Understanding the predictions of a machine learning model can ... | 2019 | 478 |
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