Advances in Neural Information Processing Systems 29

NIPS 2016

Edited by D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett

ISBN: 9781510838819

569 papers

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
Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, ecprice, Nati Srebro
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray
Ladder Variational Autoencoders Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther
Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn
Learning Sparse Gaussian Graphical Models with Overlapping Blocks Mohammad Javad Hosseini, Su-In Lee
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Kevin Winner, Daniel R. Sheldon
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation Emmanuel Abbe, Colin Sandon
A Unified Approach for Learning the Parameters of Sum-Product Networks Han Zhao, Pascal Poupart, Geoffrey J. Gordon
The Multiscale Laplacian Graph Kernel Risi Kondor, Horace Pan
Learning the Number of Neurons in Deep Networks Jose M Alvarez, Mathieu Salzmann
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition Jinzhuo Wang, Wenmin Wang, xiongtao Chen, Ronggang Wang, Wen Gao
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes Chris Junchi Li, Zhaoran Wang, Han Liu
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments Ransalu Senanayake, Lionel Ott, Simon O'Callaghan, Fabio T Ramos
CRF-CNN: Modeling Structured Information in Human Pose Estimation Xiao Chu, Wanli Ouyang, hongsheng Li, Xiaogang Wang
Bayesian latent structure discovery from multi-neuron recordings Scott Linderman, Ryan P. Adams, Jonathan W Pillow
Latent Attention For If-Then Program Synthesis Chang Liu, Xinyun Chen, Eui Chul Shin, Mingcheng Chen, Dawn Song
Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Wasserstein Training of Restricted Boltzmann Machines Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi
A primal-dual method for conic constrained distributed optimization problems Necdet Serhat Aybat, Erfan Yazdandoost Hamedani
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro
Communication-Optimal Distributed Clustering Jiecao Chen, He Sun, David Woodruff, Qin Zhang
Boosting with Abstention Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao, Evan W Archer, Liam Paninski, John P. Cunningham
Rényi Divergence Variational Inference Yingzhen Li, Richard E Turner
Stochastic Gradient Geodesic MCMC Methods Chang Liu, Jun Zhu, Yang Song
A posteriori error bounds for joint matrix decomposition problems Nicolo Colombo, Nikos Vlassis
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu, Daniel J. Hsu, Arian Maleki
Stochastic Structured Prediction under Bandit Feedback Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo
Estimating the class prior and posterior from noisy positives and unlabeled data Shantanu Jain, Martha White, Predrag Radivojac
A Minimax Approach to Supervised Learning Farzan Farnia, David Tse
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning Jean-Bastien Grill, Michal Valko, Remi Munos
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages Yin Cheng Ng, Pawel M Chilinski, Ricardo Silva
Improved Dropout for Shallow and Deep Learning Zhe Li, Boqing Gong, Tianbao Yang
Clustering Signed Networks with the Geometric Mean of Laplacians Pedro Mercado, Francesco Tudisco, Matthias Hein
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random Ilya Shpitser
Discriminative Gaifman Models Mathias Niepert
Selective inference for group-sparse linear models Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
Automated scalable segmentation of neurons from multispectral images Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward Boyden, Liam Paninski
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Composing graphical models with neural networks for structured representations and fast inference Matthew J Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, Sandeep R Datta
SoundNet: Learning Sound Representations from Unlabeled Video Yusuf Aytar, Carl Vondrick, Antonio Torralba
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep K Ravikumar, Inderjit S Dhillon
Deep Learning for Predicting Human Strategic Behavior Jason S Hartford, James R Wright, Kevin Leyton-Brown
Online and Differentially-Private Tensor Decomposition Yining Wang, Anima Anandkumar
Multivariate tests of association based on univariate tests Ruth Heller, Yair Heller
Variational Information Maximization for Feature Selection Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Learning Bound for Parameter Transfer Learning Wataru Kumagai
Matrix Completion has No Spurious Local Minimum Rong Ge, Jason Lee, Tengyu Ma
Deep Submodular Functions: Definitions and Learning Brian W Dolhansky, Jeff A. Bilmes
Adaptive optimal training of animal behavior Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan W Pillow
Structured Matrix Recovery via the Generalized Dantzig Selector Sheng Chen, Arindam Banerjee
Robust k-means: a Theoretical Revisit ALEXANDROS GEORGOGIANNIS
Tree-Structured Reinforcement Learning for Sequential Object Localization Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities Michalis Titsias RC AUEB
Poisson-Gamma dynamical systems Aaron Schein, Hanna Wallach, Mingyuan Zhou
Convergence guarantees for kernel-based quadrature rules in misspecified settings Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu
Maximization of Approximately Submodular Functions Thibaut Horel, Yaron Singer
Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou, Costas J Spanos
Linear Feature Encoding for Reinforcement Learning Zhao Song, Ronald E Parr, Xuejun Liao, Lawrence Carin
Mixed Linear Regression with Multiple Components Kai Zhong, Prateek Jain, Inderjit S Dhillon
Unsupervised Learning of Spoken Language with Visual Context David Harwath, Antonio Torralba, James Glass
Crowdsourced Clustering: Querying Edges vs Triangles Ramya Korlakai Vinayak, Babak Hassibi
Learning feed-forward one-shot learners Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi
Reshaped Wirtinger Flow for Solving Quadratic System of Equations Huishuai Zhang, Yingbin Liang
Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik
PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li, Suvrit Sra, Stefanie Jegelka
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction Hsiang-Fu Yu, Nikhil Rao, Inderjit S Dhillon
FPNN: Field Probing Neural Networks for 3D Data Yangyan Li, Soeren Pirk, Hao Su, Charles R Qi, Leonidas Guibas
Object based Scene Representations using Fisher Scores of Local Subspace Projections Mandar D Dixit, Nuno Vasconcelos
Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray, Sidhant Misra, Andrey Lokhov, Michael Chertkov
Community Detection on Evolving Graphs Aris Anagnostopoulos, Jakub Łącki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian
Preference Completion from Partial Rankings Suriya Gunasekar, Oluwasanmi O Koyejo, Joydeep Ghosh
“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation Wen-Hao Zhang, He Wang, K. Y. Michael Wong, Si Wu
A Consistent Regularization Approach for Structured Prediction Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
Fast recovery from a union of subspaces Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
Improved Techniques for Training GANs Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, Xi Chen
Coordinate-wise Power Method Qi Lei, Kai Zhong, Inderjit S Dhillon
On Mixtures of Markov Chains Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii
Near-Optimal Smoothing of Structured Conditional Probability Matrices Moein Falahatgar, Mesrob I Ohannessian, Alon Orlitsky
Dynamic Filter Networks Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc V. Gool
Estimating the Size of a Large Network and its Communities from a Random Sample Lin Chen, Amin Karbasi, Forrest W. Crawford
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back Vitaly Feldman
Nested Mini-Batch K-Means James Newling, François Fleuret
Infinite Hidden Semi-Markov Modulated Interaction Point Process matt zhang, Peng Lin, Peng Lin, Ting Guo, Yang Wang, Yang Wang, Fang Chen
A Credit Assignment Compiler for Joint Prediction Kai-Wei Chang, He He, Stephane Ross, Hal Daume III, John Langford
Deep Exploration via Bootstrapped DQN Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods Cristina Savin, Gasper Tkacik
Structured Sparse Regression via Greedy Hard Thresholding Prateek Jain, Nikhil Rao, Inderjit S Dhillon
A Multi-Batch L-BFGS Method for Machine Learning Albert S Berahas, Jorge Nocedal, Martin Takac
Cooperative Graphical Models Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
What Makes Objects Similar: A Unified Multi-Metric Learning Approach Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou
Matching Networks for One Shot Learning Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, Daan Wierstra
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares Rong Zhu
Accelerating Stochastic Composition Optimization Mengdi Wang, Ji Liu, Ethan Fang
Variational Inference in Mixed Probabilistic Submodular Models Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
The Limits of Learning with Missing Data Brian Bullins, Elad Hazan, Tomer Koren
Clustering with Same-Cluster Queries Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
Deconvolving Feedback Loops in Recommender Systems Ayan Sinha, David F Gleich, Karthik Ramani
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates Yuanzhi Li, Yingyu Liang, Andrej Risteski
Threshold Learning for Optimal Decision Making Nathan F Lepora
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making Himabindu Lakkaraju, Jure Leskovec
Measuring Neural Net Robustness with Constraints Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi
The Power of Adaptivity in Identifying Statistical Alternatives Kevin G. Jamieson, Daniel Haas, Benjamin Recht
Adaptive Skills Adaptive Partitions (ASAP) Daniel J Mankowitz, Timothy A Mann, Shie Mannor
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease Hao Zhou, Vamsi K Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C Johnson
Review Networks for Caption Generation Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan Salakhutdinov
Distributed Flexible Nonlinear Tensor Factorization Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani
Safe Policy Improvement by Minimizing Robust Baseline Regret Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes Matteo Turchetta, Felix Berkenkamp, Andreas Krause
Multimodal Residual Learning for Visual QA Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, Eric P Xing
On Regularizing Rademacher Observation Losses Richard Nock
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification Steven Cheng-Xian Li, Benjamin M. Marlin
Learning User Perceived Clusters with Feature-Level Supervision Ting-Yu Cheng, Guiguan Lin, xinyang gong, Kang-Jun Liu, Shan-Hung (Brandon) Wu
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes Hassan A Kingravi, Harshal R Maske, Girish Chowdhary
Hierarchical Question-Image Co-Attention for Visual Question Answering Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh
Double Thompson Sampling for Dueling Bandits Huasen Wu, Xin Liu
A state-space model of cross-region dynamic connectivity in MEG/EEG Ying Yang, Elissa Aminoff, Michael Tarr, Robert E Kass
Using Fast Weights to Attend to the Recent Past Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu
High-Rank Matrix Completion and Clustering under Self-Expressive Models Ehsan Elhamifar
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Alejandro Ribeiro
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale Firas Abuzaid, Joseph K. Bradley, Feynman T Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S Talwalkar
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Nguyen Cuong, Huan Xu
Guided Policy Search via Approximate Mirror Descent William H Montgomery, Sergey Levine
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability Guillaume Papa, Aurélien Bellet, Stephan Clémençon
Geometric Dirichlet Means Algorithm for topic inference Mikhail Yurochkin, Xuanlong Nguyen
Learned Region Sparsity and Diversity Also Predicts Visual Attention Zijun Wei, Hossein Adeli, Minh Hoai Nguyen, Greg Zelinsky, Dimitris Samaras
Deep Learning Models of the Retinal Response to Natural Scenes Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
Inference by Reparameterization in Neural Population Codes Rajkumar Vasudeva Raju, Zachary Pitkow
Blind Attacks on Machine Learners Alex Beatson, Zhaoran Wang, Han Liu
Learning Deep Parsimonious Representations Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun
Scalable Adaptive Stochastic Optimization Using Random Projections Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M Buhmann, Nicolai Meinshausen
Graphons, mergeons, and so on! Justin Eldridge, Mikhail Belkin, Yusu Wang
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, Adam T Kalai
Memory-Efficient Backpropagation Through Time Audrunas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman
Differential Privacy without Sensitivity Kentaro Minami, HItomi Arai, Issei Sato, Hiroshi Nakagawa
Adaptive Smoothed Online Multi-Task Learning Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats Pulkit Tandon, Yash H Malviya, Bipin Rajendran
Optimal Cluster Recovery in the Labeled Stochastic Block Model Se-Young Yun, Alexandre Proutiere
Relevant sparse codes with variational information bottleneck Matthew Chalk, Olivier Marre, Gasper Tkacik
Learning What and Where to Draw Scott E Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
A Bio-inspired Redundant Sensing Architecture Anh Tuan Nguyen, Jian Xu, Zhi Yang
Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter
Statistical Inference for Cluster Trees Jisu KIM, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman
Combinatorial Multi-Armed Bandit with General Reward Functions Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu
Learning Sensor Multiplexing Design through Back-propagation Ayan Chakrabarti
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky
VIME: Variational Information Maximizing Exploration Rein Houthooft, Xi Chen, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity Amit Daniely, Roy Frostig, Yoram Singer
Unsupervised Domain Adaptation with Residual Transfer Networks Mingsheng Long, Han Zhu, Jianmin Wang, Michael I Jordan
Stochastic Gradient MCMC with Stale Gradients Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin
Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S Du, Barnabas Poczos
Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony, Anqi Liu, Kaiser Asif, Brian Ziebart
Long-term Causal Effects via Behavioral Game Theory Panagiotis Toulis, David C. Parkes
Sampling for Bayesian Program Learning Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Unifying Count-Based Exploration and Intrinsic Motivation Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P Xing
Observational-Interventional Priors for Dose-Response Learning Ricardo Silva
Improved Error Bounds for Tree Representations of Metric Spaces Samir Chowdhury, Facundo Mémoli, Zane T Smith
A Bayesian method for reducing bias in neural representational similarity analysis Mingbo Cai, Nicolas W Schuck, Jonathan W Pillow, Yael Niv
Multistage Campaigning in Social Networks Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha
Conditional Image Generation with PixelCNN Decoders Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, Alex Graves
Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Tensor Switching Networks Chuan-Yung Tsai, Andrew M Saxe, Andrew M Saxe, David Cox
Optimistic Bandit Convex Optimization Scott Yang, Mehryar Mohri
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories Yuan Zhao, Ill Memming Park
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu, Dilin Wang
Learning in Games: Robustness of Fast Convergence Dylan J Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore, Tor Lattimore, Mark D. Reid
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami
Universal Correspondence Network Christopher B Choy, JunYoung Gwak, Silvio Savarese, Manmohan Chandraker
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games Sougata Chaudhuri, Ambuj Tewari
Showing versus doing: Teaching by demonstration Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, Joseph L Austerweil
Learning Transferrable Representations for Unsupervised Domain Adaptation Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese
On Robustness of Kernel Clustering Bowei Yan, Purnamrita Sarkar
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$ Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang
Fast Algorithms for Robust PCA via Gradient Descent Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis
Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, Mikhail Volkov, Daniela Rus
Doubly Convolutional Neural Networks Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang, Weining Lu
Brains on Beats Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven
Local Minimax Complexity of Stochastic Convex Optimization sabyasachi chatterjee, John C. Duchi, John Lafferty, Yuancheng Zhu
Kronecker Determinantal Point Processes Zelda E. Mariet, Suvrit Sra
Normalized Spectral Map Synchronization Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi
Error Analysis of Generalized Nyström Kernel Regression Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai
Regularized Nonlinear Acceleration Damien Scieur, Alexandre d'Aspremont, Francis Bach
Pairwise Choice Markov Chains Stephen Ragain, Johan Ugander
Stochastic Variational Deep Kernel Learning Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P Xing
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Tengyu Ma
DISCO Nets : DISsimilarity COefficients Networks Diane Bouchacourt, Pawan K Mudigonda, Sebastian Nowozin
Learning to Poke by Poking: Experiential Learning of Intuitive Physics Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, Sergey Levine
Value Iteration Networks Aviv Tamar, YI WU, Garrett Thomas, Sergey Levine, Pieter Abbeel
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images Junhua Mao, Jiajing Xu, Kevin Jing, Alan L. Yuille
Simple and Efficient Weighted Minwise Hashing Anshumali Shrivastava
Cooperative Inverse Reinforcement Learning Dylan Hadfield-Menell, Stuart Russell, Pieter Abbeel, Anca Dragan
Safe and Efficient Off-Policy Reinforcement Learning Remi Munos, Tom Stepleton, Anna Harutyunyan, Marc Bellemare
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu
Deep Learning Games Dale Schuurmans, Martin A Zinkevich
Strategic Attentive Writer for Learning Macro-Actions Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, koray kavukcuoglu
Clustering with Bregman Divergences: an Asymptotic Analysis Chaoyue Liu, Mikhail Belkin
Swapout: Learning an ensemble of deep architectures Saurabh Singh, Derek Hoiem, David Forsyth
Stochastic Online AUC Maximization Yiming Ying, Longyin Wen, Siwei Lyu
Optimizing affinity-based binary hashing using auxiliary coordinates Ramin Raziperchikolaei, Miguel A. Carreira-Perpinan
Sample Complexity of Automated Mechanism Design Maria-Florina F Balcan, Tuomas Sandholm, Ellen Vitercik
LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain Zeyuan Allen-Zhu, Yuanzhi Li
A Probabilistic Framework for Deep Learning Ankit B Patel, Minh Tan Nguyen, Richard Baraniuk
Without-Replacement Sampling for Stochastic Gradient Methods Ohad Shamir
Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, Shimon Whiteson
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel
Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian
The Power of Optimization from Samples Eric Balkanski, Aviad Rubinstein, Yaron Singer
New Liftable Classes for First-Order Probabilistic Inference Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole
Optimal Tagging with Markov Chain Optimization Nir Rosenfeld, Amir Globerson
Fast and Flexible Monotonic Functions with Ensembles of Lattices Mahdi Milani Fard, Kevin Canini, Andrew Cotter, Jan Pfeifer, Maya Gupta
A scaled Bregman theorem with applications Richard Nock, Aditya Menon, Cheng Soon Ong
The Product Cut Thomas Laurent, James von Brecht, Xavier Bresson, arthur szlam
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan M Rogers, Aaron Roth, Steven Z. Wu
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization Tyler B Johnson, Carlos Guestrin
Large-Scale Price Optimization via Network Flow Shinji Ito, Ryohei Fujimaki
Generative Adversarial Imitation Learning Jonathan Ho, Stefano Ermon
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Nearly Isometric Embedding by Relaxation James McQueen, Marina Meila, Dominique Joncas
DECOrrelated feature space partitioning for distributed sparse regression Xiangyu Wang, David B Dunson, Chenlei Leng
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Shizhong Han, Zibo Meng, AHMED-SHEHAB KHAN, Yan Tong
An urn model for majority voting in classification ensembles Victor Soto, Alberto Suárez, Gonzalo Martinez-Muñoz
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA Aapo Hyvarinen, Hiroshi Morioka
Leveraging Sparsity for Efficient Submodular Data Summarization Erik Lindgren, Shanshan Wu, Alexandros G Dimakis
Mistake Bounds for Binary Matrix Completion Mark Herbster, Stephen Pasteris, Massimiliano Pontil
Quantum Perceptron Models Ashish Kapoor, Nathan Wiebe, Krysta Svore
Direct Feedback Alignment Provides Learning in Deep Neural Networks Arild Nøkland
Average-case hardness of RIP certification Tengyao Wang, Quentin Berthet, Yaniv Plan
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov
Fast and Provably Good Seedings for k-Means Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Kejun Huang, Xiao Fu, Nikolaos D. Sidiropoulos
High Dimensional Structured Superposition Models Qilong Gu, Arindam Banerjee
A Bandit Framework for Strategic Regression Yang Liu, Yiling Chen
Linear Relaxations for Finding Diverse Elements in Metric Spaces Aditya Bhaskara, Mehrdad Ghadiri, Vahab Mirrokni, Ola Svensson
Binarized Neural Networks Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
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

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