Advances in Neural Information Processing Systems 29 (NIPS 2016)

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• Eliciting Categorical Data for Optimal Aggregation Chien-Ju Ho, Rafael Frongillo, Yiling Chen
• A Locally Adaptive Normal Distribution Georgios Arvanitidis, Lars K. Hansen, Søren Hauberg
• Tagger: Deep Unsupervised Perceptual Grouping Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Jürgen Schmidhuber
• Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics Wei-Shou Hsu, Pascal Poupart
• Conditional Generative Moment-Matching Networks Yong Ren, Jun Zhu, Jialian Li, Yucen Luo
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks Hao Wang, Xingjian SHI, Dit-Yan Yeung
• Bayesian Intermittent Demand Forecasting for Large Inventories Matthias W Seeger, David Salinas, Valentin Flunkert
• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Tianfan Xue, Jiajun Wu, Katherine Bouman, Bill Freeman
• Achieving budget-optimality with adaptive schemes in crowdsourcing Ashish Khetan, Sewoong Oh
• Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
• Generating Videos with Scene Dynamics Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
• Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods Andrej Risteski, Yuanzhi Li
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst
• Fast Distributed Submodular Cover: Public-Private Data Summarization Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi
• Exponential Family Embeddings Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei
• A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela van der Schaar
• Integrated perception with recurrent multi-task neural networks Hakan Bilen, Andrea Vedaldi
• Dialog-based Language Learning Jason E Weston
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal, Zoubin Ghahramani
• Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks Noah Apthorpe, Alexander Riordan, Robert Aguilar, Jan Homann, Yi Gu, David Tank, H. Sebastian Seung
• Convolutional Neural Fabrics Shreyas Saxena, Jakob Verbeek
• Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii, Hisashi Kashima
• An equivalence between high dimensional Bayes optimal inference and M-estimation Madhu Advani, Surya Ganguli
• A Sparse Interactive Model for Matrix Completion with Side Information Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi
• Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari, Nitish Korula, Vahab Mirrokni
• Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P Chwialkowski, Arthur Gretton
• Finding significant combinations of features in the presence of categorical covariates Laetitia Papaxanthos, Felipe Llinares-López, Dean Bodenham, Karsten Borgwardt
• A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing Ming Lin, Jieping Ye
• Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction Jacob Steinhardt, Gregory Valiant, Moses Charikar
• Threshold Bandits, With and Without Censored Feedback Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
• Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon
• Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models Juho Lee, Lancelot F James, Seungjin Choi
• Maximal Sparsity with Deep Networks? Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang
• Single-Image Depth Perception in the Wild Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
• Single Pass PCA of Matrix Products Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G Dimakis
• Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail, Christos Boutsidis
• Measuring the reliability of MCMC inference with bidirectional Monte Carlo Roger B Grosse, Siddharth Ancha, Daniel M. Roy
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections Xiaojiao Mao, Chunhua Shen, Yu-Bin Yang
• On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege, Pierre-Louis Giscard, Richard Wilson
• Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata, Makoto Yamada
• Optimal Architectures in a Solvable Model of Deep Networks Jonathan Kadmon, Haim Sompolinsky
• Efficient state-space modularization for planning: theory, behavioral and neural signatures Daniel McNamee, Daniel M. Wolpert, Mate Lengyel
• A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu
• Supervised Word Mover's Distance Gao Huang, Chuan Guo, Matt J Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
• Fast and accurate spike sorting of high-channel count probes with KiloSort Marius Pachitariu, Nicholas A Steinmetz, Shabnam N Kadir, Matteo Carandini, Kenneth D Harris
• Learning brain regions via large-scale online structured sparse dictionary learning Elvis DOHMATOB, Arthur Mensch, Gael Varoquaux, Bertrand Thirion
• Improving PAC Exploration Using the Median Of Means Jason Pazis, Ronald E Parr, Jonathan P How
• Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner
• Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs Yu-Xiong Wang, Martial Hebert
• Learning Bayesian networks with ancestral constraints Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche
• Exponential expressivity in deep neural networks through transient chaos Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli
• MetaGrad: Multiple Learning Rates in Online Learning Tim van Erven, Wouter M. Koolen
• Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang, Martin Paulus
• End-to-End Goal-Driven Web Navigation Rodrigo Nogueira, Kyunghyun Cho
• Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
• Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford
• Professor Forcing: A New Algorithm for Training Recurrent Networks Alex M Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio
• Deep ADMM-Net for Compressive Sensing MRI yan yang, Jian Sun, Huibin Li, Zongben Xu
• Adaptive Averaging in Accelerated Descent Dynamics Walid Krichene, Alexandre Bayen, Peter L Bartlett
• Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis Yoshinobu Kawahara
• Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J Tibshirani
• Hardness of Online Sleeping Combinatorial Optimization Problems Satyen Kale, Chansoo Lee, David Pal
• Density Estimation via Discrepancy Based Adaptive Sequential Partition Dangna Li, Kun Yang, Wing Hung Wong
• Quantized Random Projections and Non-Linear Estimation of Cosine Similarity Ping Li, Michael Mitzenmacher, Martin Slawski
• Algorithms and matching lower bounds for approximately-convex optimization Andrej Risteski, Yuanzhi Li
• The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Jian Wu, Peter Frazier
• Edge-exchangeable graphs and sparsity Diana Cai, Trevor Campbell, Tamara Broderick
• Stochastic Variance Reduction Methods for Saddle-Point Problems Balamurugan Palaniappan, Francis Bach
• A Probabilistic Model of Social Decision Making based on Reward Maximization Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao
• Bootstrap Model Aggregation for Distributed Statistical Learning JUN HAN, Qiang Liu
• Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess
• beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data Valentina Zantedeschi, Rémi Emonet, Marc Sebban
• Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii
• Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Antoine Gautier, Quynh N Nguyen, Matthias Hein
• Optimal Black-Box Reductions Between Optimization Objectives Zeyuan Allen-Zhu, Elad Hazan
• Sequential Neural Models with Stochastic Layers Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther
• Iterative Refinement of the Approximate Posterior for Directed Belief Networks Devon Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince Calhoun, Junyoung Chung
• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
• Learning shape correspondence with anisotropic convolutional neural networks Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael Bronstein
• Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola
• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter Stewart
• PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford
• Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi, D. Phoenix, Dileep George
• Probabilistic Linear Multistep Methods Onur Teymur, Kostas Zygalakis, Ben Calderhead
• Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan, Sewoong Oh
• Split LBI: An Iterative Regularization Path with Structural Sparsity Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
• Incremental Variational Sparse Gaussian Process Regression Ching-An Cheng, Byron Boots
• Sublinear Time Orthogonal Tensor Decomposition Zhao Song, David Woodruff, Huan Zhang
• Mapping Estimation for Discrete Optimal Transport Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
• Greedy Feature Construction Dino Oglic, Thomas Gärtner
• Dynamic Network Surgery for Efficient DNNs Yiwen Guo, Anbang Yao, Yurong Chen
• Graph Clustering: Block-models and model free results Yali Wan, Marina Meila
• CMA-ES with Optimal Covariance Update and Storage Complexity Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
• Feature selection in functional data classification with recursive maxima hunting José L. Torrecilla, Alberto Suárez
• Cyclades: Conflict-free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I Jordan, Kannan Ramchandran, Christopher Ré
• Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J Smola
• Spectral Learning of Dynamic Systems from Nonequilibrium Data Hao Wu, Frank Noe
• Dimension-Free Iteration Complexity of Finite Sum Optimization Problems Yossi Arjevani, Ohad Shamir
• Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes
• Active Learning with Oracle Epiphany Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu
• Stochastic Optimization for Large-scale Optimal Transport Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis Bach
• The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Damek Davis, Brent Edmunds, Madeleine Udell
• Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick
• Sorting out typicality with the inverse moment matrix SOS polynomial Edouard Pauwels, Jean B Lasserre
• The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider
• Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Theodore Bluche
• k*-Nearest Neighbors: From Global to Local Oren Anava, Kfir Levy
• Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images Vladimir Golkov, Marcin J Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers
• Learnable Visual Markers Oleg Grinchuk, Vadim Lebedev, Victor Lempitsky
• Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators Shashank Singh, Barnabas Poczos
• Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher Lynn, Daniel D Lee
• Regret Bounds for Non-decomposable Metrics with Missing Labels Nagarajan Natarajan, Prateek Jain
• Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
• Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore
• Structure-Blind Signal Recovery Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski
• Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi, Samy Bengio, zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans
• Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
• An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly, Quoc V Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio
• Interaction Networks for Learning about Objects, Relations and Physics Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, koray kavukcuoglu
• Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny, Robert B. Gramacy, Stefan Wild, Sebastien Le Digabel
• Combinatorial Energy Learning for Image Segmentation Jeremy B Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel
• Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, Frank Wood
• Coin Betting and Parameter-Free Online Learning Francesco Orabona, David Pal
• Learning Deep Embeddings with Histogram Loss Evgeniya Ustinova, Victor Lempitsky
• An Efficient Streaming Algorithm for the Submodular Cover Problem Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher
• Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing Farshad Lahouti, Babak Hassibi
• Beyond Exchangeability: The Chinese Voting Process Moontae Lee, Seok Hyun Jin, David Mimno
• Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang
• Optimal spectral transportation with application to music transcription Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
• MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild Gregory Rogez, Cordelia Schmid
• A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei
• CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu
• Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang, Michael W. Mahoney, Michael Saunders, Yuekai Sun
• Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy, Thomas Brox
• Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit, Michael J Wilber, Serge Belongie
• Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri
• Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli
• Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I Jordan
• Diffusion-Convolutional Neural Networks James Atwood, Don Towsley
• Completely random measures for modelling block-structured sparse networks Tue Herlau, Mikkel N Schmidt, Morten Mørup
• Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama
• Synthesis of MCMC and Belief Propagation Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
• Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics Travis Monk, Cristina Savin, Jörg Lücke
• Disease Trajectory Maps Peter Schulam, Raman Arora
• Bayesian optimization for automated model selection Gustavo Malkomes, Charles Schaff, Roman Garnett
• Designing smoothing functions for improved worst-case competitive ratio in online optimization Reza Eghbali, Maryam Fazel
• Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
• Multi-step learning and underlying structure in statistical models Maia Fraser
• The non-convex Burer-Monteiro approach works on smooth semidefinite programs Nicolas Boumal, Vlad Voroninski, Afonso Bandeira
• Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre Bayen
• Spatiotemporal Residual Networks for Video Action Recognition Christoph Feichtenhofer, Axel Pinz, Richard Wildes
• Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack Rae, Jonathan J Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Timothy Lillicrap
• Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah Goodman
• Reconstructing Parameters of Spreading Models from Partial Observations Andrey Lokhov
• Tracking the Best Expert in Non-stationary Stochastic Environments Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
• Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu, Mladen Kolar, Varun Gupta
• Learning Structured Sparsity in Deep Neural Networks Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
• Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang, Jialei Wang, Dan Garber, Dan Garber, Nati Srebro
• How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt, Jonah G Cader, Thomas Serre
• Regret of Queueing Bandits Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai
• Dual Space Gradient Descent for Online Learning Trung Le, Tu Nguyen, Vu Nguyen, Dinh Phung
• Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon, James Demmel, Cho-Jui Hsieh
• Catching heuristics are optimal control policies Boris Belousov, Gerhard Neumann, Constantin A Rothkopf, Jan R Peters
• Online Pricing with Strategic and Patient Buyers Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum
• Optimistic Gittins Indices Eli Gutin, Vivek Farias
• Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Hongseok Namkoong, John C. Duchi
• Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation Weihao Gao, Sewoong Oh, Pramod Viswanath
• Domain Separation Networks Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan
• A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad, Vikash K Mansinghka
• Assortment Optimization Under the Mallows model Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev
• An algorithm for L1 nearest neighbor search via monotonic embedding Xinan Wang, Sanjoy Dasgupta
• Multi-armed Bandits: Competing with Optimal Sequences Zohar S Karnin, Oren Anava
• NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang
• Probing the Compositionality of Intuitive Functions Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J Gershman
• Identification and Overidentification of Linear Structural Equation Models Bryant Chen
• An Architecture for Deep, Hierarchical Generative Models Philip Bachman
• Towards Conceptual Compression Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
• Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan
• Consistent Kernel Mean Estimation for Functions of Random Variables Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O Tolstikhin, Bernhard Schölkopf
• Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta
• Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, Danny Z Chen
• SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu
• Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain, Kevin G. Jamieson, Rob Nowak
• Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
• A Simple Practical Accelerated Method for Finite Sums Aaron Defazio
• Coupled Generative Adversarial Networks Ming-Yu Liu, Oncel Tuzel
• Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya O Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf
• Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model Zhen Xu, Wen Dong, Sargur N Srihari
• Multiple-Play Bandits in the Position-Based Model Paul Lagrée, Claire Vernade, Olivier Cappe
• Learning values across many orders of magnitude Hado P van Hasselt, Arthur Guez, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, koray kavukcuoglu, Geoffrey E. Hinton
• Supervised Learning with Tensor Networks Edwin Stoudenmire, David J Schwab
• Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
• The Multiple Quantile Graphical Model Alnur Ali, J. Zico Kolter, Ryan J Tibshirani
• Orthogonal Random Features Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, Sanjiv Kumar
• Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions Yichen Wang, Nan Du, Rakshit Trivedi, Le Song
• Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
• Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky, Kwabena A. Boahen
• GAP Safe Screening Rules for Sparse-Group Lasso Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
• Local Similarity-Aware Deep Feature Embedding Chen Huang, Chen Change Loy, Xiaoou Tang
• Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvari
• Learning Multiagent Communication with Backpropagation Sainbayar Sukhbaatar, arthur szlam, Rob Fergus
• Sub-sampled Newton Methods with Non-uniform Sampling Peng Xu, Jiyan Yang, Fred Roosta, Christopher Ré, Michael W. Mahoney
• Examples are not enough, learn to criticize! Criticism for Interpretability Been Kim, Rajiv Khanna, Oluwasanmi O Koyejo
• R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai, Yi Li, Kaiming He, Jian Sun
• Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
• A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He, Yan Wang, John Hopcroft
• Privacy Odometers and Filters: Pay-as-you-Go Composition Ryan M Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan
• More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu
• Supervised learning through the lens of compression Ofir David, Shay Moran, Amir Yehudayoff
• Sparse Support Recovery with Non-smooth Loss Functions Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques
• Tractable Operations for Arithmetic Circuits of Probabilistic Models Yujia Shen, Arthur Choi, Adnan Darwiche
• Dual Learning for Machine Translation Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma
• Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang, Georgios Giannakis
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
• Only H is left: Near-tight Episodic PAC RL
• Stochastic Three-Composite Convex Minimization Alp Yurtsever, Bang Cong Vu, Volkan Cevher
• Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune
• Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Remi Lam, Karen Willcox, David H. Wolpert
• Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabas Poczos
• Learning Parametric Sparse Models for Image Super-Resolution Yongbo Li, Weisheng Dong, Xuemei Xie, GUANGMING Shi, Xin Li, Donglai Xu
• Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula jean barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová
• Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos
• Fast learning rates with heavy-tailed losses Vu C Dinh, Lam S Ho, Binh Nguyen, Duy Nguyen
• Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Liangbei Xu, Mark Davenport
• Tight Complexity Bounds for Optimizing Composite Objectives Blake E Woodworth, Nati Srebro
• A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control Ivan Herreros, Xerxes Arsiwalla, Paul Verschure
• Verification Based Solution for Structured MAB Problems Zohar S Karnin
• SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price, Alan L. Yuille
• CliqueCNN: Deep Unsupervised Exemplar Learning Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer
• Computing and maximizing influence in linear threshold and triggering models Justin T Khim, Varun Jog, Po-Ling Loh
• Data Programming: Creating Large Training Sets, Quickly Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, Christopher Ré
• Flexible Models for Microclustering with Application to Entity Resolution Brenda Betancourt, Giacomo Zanella, Jeffrey W Miller, Hanna Wallach, Abbas Zaidi, Rebecca C. Steorts
• Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah
• An ensemble diversity approach to supervised binary hashing Miguel A. Carreira-Perpinan, Ramin Raziperchikolaei
• Learning Influence Functions from Incomplete Observations Xinran He, Ke Xu, David Kempe, Yan Liu
• Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
• On the Recursive Teaching Dimension of VC Classes Xi Chen, Xi Chen, Yu Cheng, Bo Tang
• Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Timothy Rubin, Oluwasanmi O Koyejo, Michael N. Jones, Tal Yarkoni
• Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray
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• Learning a Metric Embedding for Face Recognition using the Multibatch Method Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
• Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei
• Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn, Ian Goodfellow, Sergey Levine
• Full-Capacity Unitary Recurrent Neural Networks Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, Les Atlas
• Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes Dan Garber, Dan Garber, Ofer Meshi
• Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning Wouter M. Koolen, Peter Grünwald, Tim van Erven
• A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu
• Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling Maria-Florina F Balcan, Hongyang Zhang
• Satisfying Real-world Goals with Dataset Constraints Gabriel Goh, Andrew Cotter, Maya Gupta, Michael P Friedlander
• Launch and Iterate: Reducing Prediction Churn Mahdi Milani Fard, Quentin Cormier, Kevin Canini, Maya Gupta
• Constraints Based Convex Belief Propagation Yaniv Tenzer, Alex Schwing, Kevin Gimpel, Tamir Hazan
• Data driven estimation of Laplace-Beltrami operator Frederic Chazal, Ilaria Giulini, Bertrand Michel
• The Robustness of Estimator Composition Pingfan Tang, Jeff M Phillips
• Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi
• Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
• Select-and-Sample for Spike-and-Slab Sparse Coding Abdul-Saboor Sheikh, Jörg Lücke
• Learning Infinite RBMs with Frank-Wolfe Wei Ping, Qiang Liu, Alex Ihler
• Faster Projection-free Convex Optimization over the Spectrahedron Dan Garber, Dan Garber
• Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
• Joint quantile regression in vector-valued RKHSs Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
• Kernel Bayesian Inference with Posterior Regularization Yang Song, Jun Zhu, Yong Ren
• Scaled Least Squares Estimator for GLMs in Large-Scale Problems Murat A Erdogdu, Lee H Dicker, Mohsen Bayati
• Contextual semibandits via supervised learning oracles Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik
• Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables Mauro Scanagatta, Giorgio Corani, Cassio P de Campos, Marco Zaffalon
• Unsupervised Learning from Noisy Networks with Applications to Hi-C Data Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje
• Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan D He, Christopher M De Sa, Ioannis Mitliagkas, Christopher Ré
• Deep Neural Networks with Inexact Matching for Person Re-Identification Arulkumar Subramaniam, Moitreya Chatterjee, Anurag Mittal
• Efficient Neural Codes under Metabolic Constraints Zhuo Wang, Xue-Xin Wei, Alan Stocker, Daniel D Lee
• Learning Kernels with Random Features Aman Sinha, John C. Duchi
• Combinatorial semi-bandit with known covariance Rémy Degenne, Vianney Perchet
• Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee
• Exact Recovery of Hard Thresholding Pursuit Xiaotong Yuan, Ping Li, Tong Zhang
• Parameter Learning for Log-supermodular Distributions Tatiana Shpakova, Francis Bach
• A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization Jingwei Liang, Jalal Fadili, Gabriel Peyré
• Optimal Binary Classifier Aggregation for General Losses Akshay Balsubramani, Yoav S Freund
• Dense Associative Memory for Pattern Recognition Dmitry Krotov, John J. Hopfield
• Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth
• Variational Autoencoder for Deep Learning of Images, Labels and Captions Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma
• Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu
• Disentangling factors of variation in deep representation using adversarial training Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann LeCun
• Gaussian Processes for Survival Analysis Tamara Fernandez, Nicolas Rivera, Yee Whye Teh
• Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated Namrata Vaswani, Han Guo
• On Explore-Then-Commit strategies Aurelien Garivier, Tor Lattimore, Emilie Kaufmann
• Adaptive Neural Compilation Rudy R Bunel, Alban Desmaison, Pawan K Mudigonda, Pushmeet Kohli, Philip Torr
• Graphical Time Warping for Joint Alignment of Multiple Curves Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu
• PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, Pushmeet Kohli
• DeepMath - Deep Sequence Models for Premise Selection Geoffrey Irving, Christian Szegedy, Alexander A Alemi, Niklas Een, Francois Chollet, Josef Urban
• A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf
• The Forget-me-not Process Kieran Milan, Joel Veness, James Kirkpatrick, Michael Bowling, Anna Koop, Demis Hassabis
• Unsupervised Risk Estimation Using Only Conditional Independence Structure Jacob Steinhardt, Percy Liang
• Deep Learning without Poor Local Minima Kenji Kawaguchi
• Linear Contextual Bandits with Knapsacks Shipra Agrawal, Nikhil Devanur
• High resolution neural connectivity from incomplete tracing data using nonnegative spline regression Kameron D Harris, Stefan Mihalas, Eric Shea-Brown
• Learning and Forecasting Opinion Dynamics in Social Networks Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
• Lifelong Learning with Weighted Majority Votes Anastasia Pentina, Ruth Urner
• Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich
• Ancestral Causal Inference Sara Magliacane, Tom Claassen, Joris M. Mooij
• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum
• Agnostic Estimation for Misspecified Phase Retrieval Models Matey Neykov, Zhaoran Wang, Han Liu
• Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? Arturo Deza, Miguel Eckstein
• Proximal Deep Structured Models Shenlong Wang, Sanja Fidler, Raquel Urtasun
• SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros
• On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
• The Generalized Reparameterization Gradient Francisco R Ruiz, Michalis Titsias RC AUEB, David Blei
• Semiparametric Differential Graph Models Pan Xu, Quanquan Gu
• Neural Universal Discrete Denoiser Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
• Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity Eugene Belilovsky, Gaël Varoquaux, Matthew B Blaschko
• Learning to learn by gradient descent by gradient descent Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
• Mixed vine copulas as joint models of spike counts and local field potentials Arno Onken, Stefano Panzeri
• Can Active Memory Replace Attention? Łukasz Kaiser, Samy Bengio
• Fast Active Set Methods for Online Spike Inference from Calcium Imaging Johannes Friedrich, Liam Paninski
• Human Decision-Making under Limited Time Pedro A Ortega, Alan Stocker
• End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal
• Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal, Shivani Agarwal
• Visual Question Answering with Question Representation Update (QRU) Ruiyu Li, Jiaya Jia
• Optimal Learning for Multi-pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco
• General Tensor Spectral Co-clustering for Higher-Order Data Tao Wu, Austin R Benson, David F Gleich
• Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition Ahmed M. Alaa, Mihaela van der Schaar
• Generating Long-term Trajectories Using Deep Hierarchical Networks Stephan Zheng, Yisong Yue, Jennifer Hobbs
• Natural-Parameter Networks: A Class of Probabilistic Neural Networks Hao Wang, Xingjian SHI, Dit-Yan Yeung
• Minimizing Quadratic Functions in Constant Time Kohei Hayashi, Yuichi Yoshida