Advances in Neural Information Processing Systems 30 (NIPS 2017)

The papers below appear in Advances in Neural Information Processing Systems 30 edited by I. Guyon and U.V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett.
They are proceedings from the conference, “Neural Information Processing Systems 2017.”

• Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
• Concentration of Multilinear Functions of the Ising Model with Applications to Network Data Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
• Deep Subspace Clustering Networks Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid
• Attentional Pooling for Action Recognition Rohit Girdhar, Deva Ramanan
• On the Consistency of Quick Shift Heinrich Jiang
• Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
• Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis Jian Zhao, Lin Xiong, Panasonic Karlekar Jayashree, Jianshu Li, Fang Zhao, Zhecan Wang, Panasonic Sugiri Pranata, Panasonic Shengmei Shen, Shuicheng Yan, Jiashi Feng
• Dilated Recurrent Neural Networks Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A. Hasegawa-Johnson, Thomas S. Huang
• Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs Saurabh Verma, Zhi-Li Zhang
• Scalable Generalized Linear Bandits: Online Computation and Hashing Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett
• Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models Chris Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark Girolami
• Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer
• Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
• Interactive Submodular Bandit Lin Chen, Andreas Krause, Amin Karbasi
• Learning to See Physics via Visual De-animation Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum
• Label Efficient Learning of Transferable Representations acrosss Domains and Tasks Zelun Luo, Yuliang Zou, Judy Hoffman, Li F. Fei-Fei
• Decoding with Value Networks for Neural Machine Translation Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tieyan Liu
• Parametric Simplex Method for Sparse Learning Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao
• Group Sparse Additive Machine Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang
• Uprooting and Rerooting Higher-Order Graphical Models Mark Rowland, Adrian Weller
• The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings Krzysztof M. Choromanski, Mark Rowland, Adrian Weller
• From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, Adrian Weller
• Inferring Generative Model Structure with Static Analysis Paroma Varma, Bryan D. He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel Rubin, Christopher Ré
• Structured Embedding Models for Grouped Data Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei
• A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton
• Cortical microcircuits as gated-recurrent neural networks Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, TIm Vogels
• k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms Cong Han Lim, Stephen Wright
• A simple model of recognition and recall memory Nisheeth Srivastava, Edward Vul
• On Structured Prediction Theory with Calibrated Convex Surrogate Losses Anton Osokin, Francis Bach, Simon Lacoste-Julien
• Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra
• MaskRNN: Instance Level Video Object Segmentation Yuan-Ting Hu, Jia-Bin Huang, Alexander Schwing
• Gated Recurrent Convolution Neural Network for OCR Jianfeng Wang, Xiaolin Hu
• Towards Accurate Binary Convolutional Neural Network Xiaofan Lin, Cong Zhao, Wei Pan
• Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang
• Learning a Multi-View Stereo Machine Abhishek Kar, Christian Häne, Jitendra Malik
• Phase Transitions in the Pooled Data Problem Jonathan Scarlett, Volkan Cevher
• Universal Style Transfer via Feature Transforms Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
• On the Model Shrinkage Effect of Gamma Process Edge Partition Models Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura
• Pose Guided Person Image Generation Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool
• Inference in Graphical Models via Semidefinite Programming Hierarchies Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
• Variable Importance Using Decision Trees Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
• Preventing Gradient Explosions in Gated Recurrent Units Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura
• On the Power of Truncated SVD for General High-rank Matrix Estimation Problems Simon S. Du, Yining Wang, Aarti Singh
• f-GANs in an Information Geometric Nutshell Richard Nock, Zac Cranko, Aditya K. Menon, Lizhen Qu, Robert C. Williamson
• Toward Multimodal Image-to-Image Translation Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
• Mixture-Rank Matrix Approximation for Collaborative Filtering Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen Chu
• Continuous DR-submodular Maximization: Structure and Algorithms An Bian, Kfir Levy, Andreas Krause, Joachim M. Buhmann
• Learning with Average Top-k Loss Yanbo Fan, Siwei Lyu, Yiming Ying, Baogang Hu
• Learning multiple visual domains with residual adapters Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
• Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions Ryan J. Tibshirani
• Learning Spherical Convolution for Fast Features from 360° Imagery Yu-Chuan Su, Kristen Grauman
• MarrNet: 3D Shape Reconstruction via 2.5D Sketches Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum
• Multimodal Learning and Reasoning for Visual Question Answering Ilija Ilievski, Jiashi Feng
• Adversarial Surrogate Losses for Ordinal Regression Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart
• Hypothesis Transfer Learning via Transformation Functions Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
• Controllable Invariance through Adversarial Feature Learning Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
• Convergence Analysis of Two-layer Neural Networks with ReLU Activation Yuanzhi Li, Yang Yuan
• Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki
• Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk
• Efficient Online Linear Optimization with Approximation Algorithms Dan Garber
• Geometric Descent Method for Convex Composite Minimization Shixiang Chen, Shiqian Ma, Wei Liu
• Diffusion Approximations for Online Principal Component Estimation and Global Convergence Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
• Avoiding Discrimination through Causal Reasoning Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
• Nonparametric Online Regression while Learning the Metric Ilja Kuzborskij, Nicolò Cesa-Bianchi
• Recycling Privileged Learning and Distribution Matching for Fairness Novi Quadrianto, Viktoriia Sharmanska
• Safe and Nested Subgame Solving for Imperfect-Information Games Noam Brown, Tuomas Sandholm
• Unsupervised Image-to-Image Translation Networks Ming-Yu Liu, Thomas Breuel, Jan Kautz
• Coded Distributed Computing for Inverse Problems Yaoqing Yang, Pulkit Grover, Soummya Kar
• A Screening Rule for l1-Regularized Ising Model Estimation Zhaobin Kuang, Sinong Geng, David Page
• Improved Dynamic Regret for Non-degenerate Functions Lijun Zhang, Tianbao Yang, Jinfeng Yi, Jing Rong, Zhi-Hua Zhou
• Learning Efficient Object Detection Models with Knowledge Distillation Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker
• One-Sided Unsupervised Domain Mapping Sagie Benaim, Lior Wolf
• Deep Mean-Shift Priors for Image Restoration Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin
• Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi
• A New Theory for Matrix Completion Guangcan Liu, Qingshan Liu, Xiaotong Yuan
• Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes Jeremiah Liu, Brent Coull
• Lower bounds on the robustness to adversarial perturbations Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys
• Minimizing a Submodular Function from Samples Eric Balkanski, Yaron Singer
• Introspective Classification with Convolutional Nets Long Jin, Justin Lazarow, Zhuowen Tu
• Label Distribution Learning Forests Wei Shen, KAI ZHAO, Yilu Guo, Alan L. Yuille
• Unsupervised learning of object frames by dense equivariant image labelling James Thewlis, Hakan Bilen, Andrea Vedaldi
• Compression-aware Training of Deep Networks Jose M. Alvarez, Mathieu Salzmann
• Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D. Simeral, Beata Jarosiewicz, Erik Sudderth
• PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S. Yu
• Detrended Partial Cross Correlation for Brain Connectivity Analysis Jaime Ide, Fábio Cappabianco, Fabio Faria, Chiang-shan R. Li
• Contrastive Learning for Image Captioning Bo Dai, Dahua Lin
• Safe Model-based Reinforcement Learning with Stability Guarantees Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, Andreas Krause
• Online multiclass boosting Young Hun Jung, Jack Goetz, Ambuj Tewari
• Matching on Balanced Nonlinear Representations for Treatment Effects Estimation Sheng Li, Yun Fu
• Learning Overcomplete HMMs Vatsal Sharan, Sham M. Kakade, Percy S. Liang, Gregory Valiant
• GP CaKe: Effective brain connectivity with causal kernels Luca Ambrogioni, Max Hinne, Marcel Van Gerven, Eric Maris
• Decoupling "when to update" from "how to update" Eran Malach, Shai Shalev-Shwartz
• Self-Normalizing Neural Networks Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
• Learning to Pivot with Adversarial Networks Gilles Louppe, Michael Kagan, Kyle Cranmer
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
• Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
• Differentiable Learning of Submodular Models Josip Djolonga, Andreas Krause
• Inductive Representation Learning on Large Graphs Will Hamilton, Zhitao Ying, Jure Leskovec
• Subset Selection and Summarization in Sequential Data Ehsan Elhamifar, M. Clara De Paolis Kaluza
• Question Asking as Program Generation Anselm Rothe, Brenden M. Lake, Todd Gureckis
• Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces Songbai Yan, Chicheng Zhang
• Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabas Poczos
• Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction Kristofer Bouchard, Alejandro Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya
• One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
• Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort
• Integration Methods and Optimization Algorithms Damien Scieur, Vincent Roulet, Francis Bach, Alexandre d'Aspremont
• Sharpness, Restart and Acceleration Vincent Roulet, Alexandre d'Aspremont
• Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
• Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc V. Gool
• Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard
• Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications Qinshi Wang, Wei Chen
• Predictive-State Decoders: Encoding the Future into Recurrent Networks Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris Kitani, J. Bagnell
• Optimistic posterior sampling for reinforcement learning: worst-case regret bounds Shipra Agrawal, Randy Jia
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen, Harri Valpola
• Matching neural paths: transfer from recognition to correspondence search Nikolay Savinov, Lubor Ladicky, Marc Pollefeys
• Linearly constrained Gaussian processes Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön
• Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher
• Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph J. Lim
• Learning to Inpaint for Image Compression Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani
• Adaptive Bayesian Sampling with Monte Carlo EM Anirban Roychowdhury, Srinivasan Parthasarathy
• ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
• Shape and Material from Sound Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill Freeman
• Flexible statistical inference for mechanistic models of neural dynamics Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
• Online Prediction with Selfish Experts Tim Roughgarden, Okke Schrijvers
• Tensor Biclustering Soheil Feizi, Hamid Javadi, David Tse
• DPSCREEN: Dynamic Personalized Screening Kartik Ahuja, William Zame, Mihaela van der Schaar
• Learning Unknown Markov Decision Processes: A Thompson Sampling Approach Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
• Testing and Learning on Distributions with Symmetric Noise Invariance Ho Chung Law, Christopher Yau, Dino Sejdinovic
• A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering Hongteng Xu, Hongyuan Zha
• Deanonymization in the Bitcoin P2P Network Giulia Fanti, Pramod Viswanath
• Accelerated consensus via Min-Sum Splitting Patrick Rebeschini, Sekhar C. Tatikonda
• Generalized Linear Model Regression under Distance-to-set Penalties Jason Xu, Eric Chi, Kenneth Lange
• Adaptive stimulus selection for optimizing neural population responses Benjamin Cowley, Ryan Williamson, Katerina Clemens, Matthew Smith, Byron M. Yu
• Nonbacktracking Bounds on the Influence in Independent Cascade Models Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee
• Learning with Feature Evolvable Streams Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
• Online Convex Optimization with Stochastic Constraints Hao Yu, Michael Neely, Xiaohan Wei
• Max-Margin Invariant Features from Transformed Unlabelled Data Dipan Pal, Ashwin Kannan, Gautam Arakalgud, Marios Savvides
• Regularized Modal Regression with Applications in Cognitive Impairment Prediction Xiaoqian Wang, Hong Chen, Weidong Cai, Dinggang Shen, Heng Huang
• Translation Synchronization via Truncated Least Squares Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang
• From which world is your graph Cheng Li, Felix MF Wong, Zhenming Liu, Varun Kanade
• A New Alternating Direction Method for Linear Programming Sinong Wang, Ness Shroff
• Regret Analysis for Continuous Dueling Bandit Wataru Kumagai
• Best Response Regression Omer Ben Porat, Moshe Tennenholtz
• TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
• Learning Affinity via Spatial Propagation Networks Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz
• Linear regression without correspondence Daniel J. Hsu, Kevin Shi, Xiaorui Sun
• NeuralFDR: Learning Discovery Thresholds from Hypothesis Features Fei Xia, Martin J. Zhang, James Y. Zou, David Tse
• Cost efficient gradient boosting Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler
• Probabilistic Rule Realization and Selection Haizi Yu, Tianxi Li, Lav R. Varshney
• Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions Aryeh Kontorovich, Sivan Sabato, Roi Weiss
• A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis Tor Lattimore
• Learning Multiple Tasks with Multilinear Relationship Networks Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Philip S. Yu
• Deep Hyperalignment Muhammad Yousefnezhad, Daoqiang Zhang
• Online to Offline Conversions, Universality and Adaptive Minibatch Sizes Kfir Levy
• Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure Alberto Bietti, Julien Mairal
• Deep Learning with Topological Signatures Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl
• Predicting User Activity Level In Point Processes With Mass Transport Equation Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song
• Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff
• Deep Dynamic Poisson Factorization Model ChengYue Gong, win-bin huang
• Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis, Masashi Sugiyama
• Optimal Sample Complexity of M-wise Data for Top-K Ranking Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh
• Reliable Decision Support using Counterfactual Models Peter Schulam, Suchi Saria
• QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
• Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks Ziming Zhang, Matthew Brand
• Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer, Itay Hubara, Daniel Soudry
• Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks Urs Köster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William Constable, Oguz Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao
• Model evidence from nonequilibrium simulations Michael Habeck
• Minimal Exploration in Structured Stochastic Bandits Richard Combes, Stefan Magureanu, Alexandre Proutiere
• Learned D-AMP: Principled Neural Network based Compressive Image Recovery Chris Metzler, Ali Mousavi, Richard Baraniuk
• Deliberation Networks: Sequence Generation Beyond One-Pass Decoding Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, Tie-Yan Liu
• Adaptive Clustering through Semidefinite Programming Martin Royer
• Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning Liangpeng Zhang, Ke Tang, Xin Yao
• Repeated Inverse Reinforcement Learning Kareem Amin, Nan Jiang, Satinder Singh
• The Numerics of GANs Lars Mescheder, Sebastian Nowozin, Andreas Geiger
• Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search Luigi Acerbi, Wei Ji
• Learning Chordal Markov Networks via Branch and Bound Kari Rantanen, Antti Hyttinen, Matti Järvisalo
• Revenue Optimization with Approximate Bid Predictions Andres Munoz, Sergei Vassilvitskii
• Solving Most Systems of Random Quadratic Equations Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen
• Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu, Yu Zhang, James Glass
• Lookahead Bayesian Optimization with Inequality Constraints Remi Lam, Karen Willcox
• Hierarchical Methods of Moments Matteo Ruffini, Guillaume Rabusseau, Borja Balle
• Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts Raymond Yeh, Jinjun Xiong, Wen-Mei Hwu, Minh Do, Alexander Schwing
• Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network Lixin Fan
• Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization Pan Xu, Jian Ma, Quanquan Gu
• Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models Sergey Ioffe
• Generating steganographic images via adversarial training Jamie Hayes, George Danezis
• Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason Altschuler, Jonathan Weed, Philippe Rigollet
• PixelGAN Autoencoders Alireza Makhzani, Brendan J. Frey
• Consistent Multitask Learning with Nonlinear Output Relations Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
• Alternating minimization for dictionary learning with random initialization Niladri Chatterji, Peter L. Bartlett
• Learning ReLUs via Gradient Descent Mahdi Soltanolkotabi
• Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann
• Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems Le Fang, Fan Yang, Wen Dong, Tong Guan, Chunming Qiao
• Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs Rowan McAllister, Carl Edward Rasmussen
• Compatible Reward Inverse Reinforcement Learning Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli
• First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization Aryan Mokhtari, Alejandro Ribeiro
• Hiding Images in Plain Sight: Deep Steganography Shumeet Baluja
• Neural Program Meta-Induction Jacob Devlin, Rudy R. Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli
• Bayesian Dyadic Trees and Histograms for Regression Stéphanie van der Pas, Veronika Rockova
• A graph-theoretic approach to multitasking Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip dey, Kayhan Ozcimder
• Consistent Robust Regression Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar
• Natural Value Approximators: Learning when to Trust Past Estimates Zhongwen Xu, Joseph Modayil, Hado P. van Hasselt, Andre Barreto, David Silver, Tom Schaul
• Bandits Dueling on Partially Ordered Sets Julien Audiffren, Liva Ralaivola
• Elementary Symmetric Polynomials for Optimal Experimental Design Zelda E. Mariet, Suvrit Sra
• Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols Serhii Havrylov, Ivan Titov
• Training Deep Networks without Learning Rates Through Coin Betting Francesco Orabona, Tatiana Tommasi
• Pixels to Graphs by Associative Embedding Alejandro Newell, Jia Deng
• Runtime Neural Pruning Ji Lin, Yongming Rao, Jiwen Lu, Jie Zhou
• Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks Surbhi Goel, Adam Klivans
• MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabas Poczos
• The Reversible Residual Network: Backpropagation Without Storing Activations Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
• Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe Quentin Berthet, Vianney Perchet
• Zap Q-Learning Adithya M Devraj, Sean Meyn
• Expectation Propagation for t-Exponential Family Using q-Algebra Futoshi Futami, Issei Sato, Masashi Sugiyama
• Few-Shot Learning Through an Information Retrieval Lens Eleni Triantafillou, Richard Zemel, Raquel Urtasun
• Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein, Maksym Andriushchenko
• Associative Embedding: End-to-End Learning for Joint Detection and Grouping Alejandro Newell, Zhiao Huang, Jia Deng
• Practical Locally Private Heavy Hitters Raef Bassily, kobbi nissim, Uri Stemmer, Abhradeep Guha Thakurta
• Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences Kinjal Basu, Ankan Saha, Shaunak Chatterjee
• Inhomogeneous Hypergraph Clustering with Applications Pan Li, Olgica Milenkovic
• Differentiable Learning of Logical Rules for Knowledge Base Reasoning Fan Yang, Zhilin Yang, William W. Cohen
• Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
• Masked Autoregressive Flow for Density Estimation George Papamakarios, Iain Murray, Theo Pavlakou
• Non-convex Finite-Sum Optimization Via SCSG Methods Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan
• Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting Rebecca Morrison, Ricardo Baptista, Youssef Marzouk
• An inner-loop free solution to inverse problems using deep neural networks Kai Fan, Qi Wei, Lawrence Carin, Katherine A. Heller
• OnACID: Online Analysis of Calcium Imaging Data in Real Time Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis
• Collaborative PAC Learning Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao
• Fast Black-box Variational Inference through Stochastic Trust-Region Optimization Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
• Scalable Demand-Aware Recommendation Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li
• SGD Learns the Conjugate Kernel Class of the Network Amit Daniely
• Noise-Tolerant Interactive Learning Using Pairwise Comparisons Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski
• Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems Yonatan Belinkov, James Glass
• Generative Local Metric Learning for Kernel Regression Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D. Lee
• Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications Linus Hamilton, Frederic Koehler, Ankur Moitra
• Fitting Low-Rank Tensors in Constant Time Kohei Hayashi, Yuichi Yoshida
• Deep Supervised Discrete Hashing Qi Li, Zhenan Sun, Ran He, Tieniu Tan
• Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation Zhaohan Guo, Philip S. Thomas, Emma Brunskill
• How regularization affects the critical points in linear networks Amirhossein Taghvaei, Jin W. Kim, Prashant Mehta
• Fisher GAN Youssef Mroueh, Tom Sercu
• Information-theoretic analysis of generalization capability of learning algorithms Aolin Xu, Maxim Raginsky
• Sparse Approximate Conic Hulls Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi
• Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter
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