NIPS 2016 Accepted Papers
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- Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Bryan He*, Stanford University; Christopher De Sa, Stanford University; Ioannis Mitliagkas, ; Christopher Ré, Stanford University
- Deep ADMM-Net for Compressive Sensing MRI
Yan Yang, Xi'an Jiaotong University; Jian Sun*, Xi'an Jiaotong University; Huibin Li, ; Zongben Xu,
- A scaled Bregman theorem with applications
Richard NOCK, Data61 and ANU; Aditya Menon*, ; Cheng Soon Ong, Data61
- Swapout: Learning an ensemble of deep architectures
Saurabh Singh*, UIUC; Derek Hoiem, UIUC; David Forsyth, UIUC
- On Regularizing Rademacher Observation Losses
Richard NOCK*, Data61 and ANU
- Without-Replacement Sampling for Stochastic Gradient Methods
Ohad Shamir*, Weizmann Institute of Science
- Fast and Provably Good Seedings for k-Means
Olivier Bachem*, ETH Zurich; Mario Lucic, ETH Zurich; Hamed Hassani, ETH Zurich; Andreas Krause,
- Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn*, Google, Inc.; Ian Goodfellow, ; Sergey Levine, University of Washington
- Matrix Completion and Clustering in Self-Expressive Models
Ehsan Elhamifar*,
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Chengkai Zhang, ; Jiajun Wu*, MIT; Tianfan Xue, ; William Freeman, ; Joshua Tenenbaum,
- Probabilistic Modeling of Future Frames from a Single Image
Tianfan Xue*, ; Jiajun Wu, MIT; Katherine Bouman, MIT; William Freeman,
- Human Decision-Making under Limited Time
Pedro Ortega*, ; Alan Stocker,
- Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Shizhong Han*, University of South Carolina; Zibo Meng, University of South Carolina; Ahmed Shehab Khan, University of South Carolina; Yan Tong, University of South Carolina
- Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung,
- Tree-Structured Reinforcement Learning for Sequential Object Localization
Zequn Jie*, National Univ of Singapore; Xiaodan Liang, Sun Yat-sen University; Jiashi Feng, National University of Singapo; Xiaojie Jin, NUS; Wen Feng Lu, National Univ of Singapore; Shuicheng Yan,
- Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long*, Tsinghua University; Han Zhu, Tsinghua University; Jianmin Wang, Tsinghua University; Michael Jordan,
- Verification Based Solution for Structured MAB Problems
Zohar Karnin*,
- Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat*, UC Berkeley; Walid Krichene, UC Berkeley; Claire Tomlin, UC Berkeley; Alexandre Bayen, UC Berkeley
- Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Columbia University; Evan Archer*, ; John Cunningham, ; Liam Paninski,
- SURGE: Surface Regularized Geometry Estimation from a Single Image
Peng Wang*, UCLA; Xiaohui Shen, Adobe Research; Bryan Russell, ; Scott Cohen, Adobe Research; Brian Price, ; Alan Yuille,
- Interpretable Distribution Features with Maximum Testing Power
Wittawat Jitkrittum*, Gatsby Unit, UCL; Zoltan Szabo, ; Kacper Chwialkowski, Gatsby Unit, UCL; Arthur Gretton,
- Sorting out typicality with the inverse moment matrix SOS polynomial
Edouard Pauwels*, ; Jean-Bernard Lasserre, LAAS-CNRS
- Multi-armed Bandits: Competing with Optimal Sequences
Zohar Karnin*, ; Oren Anava, Technion
- Multivariate tests of association based on univariate tests
Ruth Heller*, Tel-Aviv University; Yair Heller,
- Learning What and Where to Draw
Scott Reed*, University of Michigan; Zeynep Akata, Max Planck Institute for Informatics; Santosh Mohan, University of MIchigan; Samuel Tenka, University of MIchigan; Bernt Schiele, ; Honglak Lee, University of Michigan
- The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
Damek Davis*, Cornell University; Brent Edmunds, University of California, Los Angeles; Madeleine Udell,
- Integrator Nets
Hakan Bilen*, University of Oxford; Andrea Vedaldi,
- Combining Low-Density Separators with CNNs
Yu-Xiong Wang*, Carnegie Mellon University; Martial Hebert, Carnegie Mellon University
- CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
Yunhe Wang*, Peking University ; Shan You, ; Dacheng Tao, ; Chao Xu, ; Chang Xu,
- Cooperative Graphical Models
Josip Djolonga*, ETH Zurich; Stefanie Jegelka, MIT; Sebastian Tschiatschek, ETH Zurich; Andreas Krause,
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin*, Microsoft Research; Botond Cseke, Microsoft Research; Ryota Tomioka, MSRC
- Bayesian Optimization for Probabilistic Programs
Tom Rainforth*, University of Oxford; Tuan Anh Le, University of Oxford; Jan-Willem van de Meent, University of Oxford; Michael Osborne, ; Frank Wood,
- Hierarchical Question-Image Co-Attention for Visual Question Answering
Jiasen Lu*, Virginia Tech; Jianwei Yang, Virginia Tech; Dhruv Batra, ; Devi Parikh, Virginia Tech
- Optimal Sparse Linear Encoders and Sparse PCA
Malik Magdon-Ismail*, Rensselaer; Christos Boutsidis,
- FPNN: Field Probing Neural Networks for 3D Data
Yangyan Li*, Stanford University; Soeren Pirk, Stanford University; Hao Su, Stanford University; Charles Qi, Stanford University; Leonidas Guibas, Stanford University
- CRF-CNN: Modeling Structured Information in Human Pose Estimation
Xiao Chu*, Cuhk; Wanli Ouyang, ; hongsheng Li, cuhk; Xiaogang Wang, Chinese University of Hong Kong
- Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, University of Pennsylvania; Michael Kearns, ; Jamie Morgenstern*, University of Pennsylvania; Aaron Roth,
- Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Alexander Kirillov*, TU Dresden; Alexander Shekhovtsov, ; Carsten Rother, ; Bogdan Savchynskyy,
- Domain Separation Networks
Dilip Krishnan, Google; George Trigeorgis, Google; Konstantinos Bousmalis*, ; Nathan Silberman, Google; Dumitru Erhan, Google
- DISCO Nets : DISsimilarity COefficients Networks
Diane Bouchacourt*, University of Oxford; M. Pawan Kumar, University of Oxford; Sebastian Nowozin,
- Multimodal Residual Learning for Visual QA
Jin-Hwa Kim*, Seoul National University; Sang-Woo Lee, Seoul National University; Dong-Hyun Kwak, Seoul National University; Min-Oh Heo, Seoul National University; Jeonghee Kim, Naver Labs; Jung-Woo Ha, Naver Labs; Byoung-Tak Zhang, Seoul National University
- CMA-ES with Optimal Covariance Update and Storage Complexity
Dídac Rodríguez Arbonès, University of Copenhagen; Oswin Krause, ; Christian Igel*,
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
Jifeng Dai, Microsoft; Yi Li, Tsinghua University; Kaiming He*, Microsoft; Jian Sun, Microsoft
- GAP Safe Screening Rules for Sparse-Group Lasso
Eugene Ndiaye, Télécom ParisTech; Olivier Fercoq, ; Alexandre Gramfort, ; Joseph Salmon*,
- Learning and Forecasting Opinion Dynamics in Social Networks
Abir De, IIT Kharagpur; Isabel Valera, ; Niloy Ganguly, IIT Kharagpur; sourangshu Bhattacharya, IIT Kharagpur; Manuel Gomez Rodriguez*, MPI-SWS
- Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
Rong Zhu*, Chinese Academy of Sciences
- Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung,
- Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
Jean Barbier, EPFL; mohamad Dia, EPFL; Florent Krzakala*, ; Thibault Lesieur, IPHT Saclay; Nicolas Macris, EPFL; Lenka Zdeborova,
- A Unified Approach for Learning the Parameters of Sum-Product Networks
Han Zhao*, Carnegie Mellon University; Pascal Poupart, ; Geoff Gordon,
- Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
Junhua Mao*, UCLA; Jiajing Xu, ; Kevin Jing, ; Alan Yuille,
- Stochastic Online AUC Maximization
Yiming Ying*, ; Longyin Wen, State University of New York at Albany; Siwei Lyu, State University of New York at Albany
- The Generalized Reparameterization Gradient
Francisco Ruiz*, Columbia University; Michalis K. Titsias, ; David Blei,
- Coupled Generative Adversarial Networks
Ming-Yu Liu*, MERL; Oncel Tuzel, Mitsubishi Electric Research Labs (MERL)
- Exponential Family Embeddings
Maja Rudolph*, Columbia University; Francisco J. R. Ruiz, ; Stephan Mandt, Disney Research; David Blei,
- Variational Information Maximization for Feature Selection
Shuyang Gao*, ; Greg Ver Steeg, ; Aram Galstyan,
- Operator Variational Inference
Rajesh Ranganath*, Princeton University; Dustin Tran, Columbia University; Jaan Altosaar, Princeton University; David Blei,
- Fast learning rates with heavy-tailed losses
Vu Dinh*, Fred Hutchinson Cancer Center; Lam Ho, UCLA; Binh Nguyen, University of Science, Vietnam; Duy Nguyen, University of Wisconsin-Madison
- Budgeted stream-based active learning via adaptive submodular maximization
Kaito Fujii*, Kyoto University; Hisashi Kashima, Kyoto University
- Learning feed-forward one-shot learners
Luca Bertinetto, University of Oxford; Joao Henriques, University of Oxford; Jack Valmadre*, University of Oxford; Philip Torr, ; Andrea Vedaldi,
- Learning User Perceived Clusters with Feature-Level Supervision
Ting-Yu Cheng, ; Kuan-Hua Lin, ; Xinyang Gong, Baidu Inc.; Kang-Jun Liu, ; Shan-Hung Wu*, National Tsing Hua University
- Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
Pan Zhang*, ITP, CAS
- Residual Networks are Exponential Ensembles of Relatively Shallow Networks
Andreas Veit*, Cornell University; Michael Wilber, ; Serge Belongie, Cornell University
- Adversarial Multiclass Classification: A Risk Minimization Perspective
Rizal Fathony*, U. of Illinois at Chicago; Anqi Liu, ; Kaiser Asif, ; Brian Ziebart,
- Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow
Gang Wang*, University of Minnesota; Georgios Giannakis, University of Minnesota
- Coin Betting and Parameter-Free Online Learning
Francesco Orabona*, Yahoo Research; David Pal,
- Deep Learning without Poor Local Minima
Kenji Kawaguchi*, MIT
- Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Eugene Belilovsky*, CentraleSupelec; Gael Varoquaux, ; Matthew Blaschko, KU Leuven
- A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++
Dennis Wei*, IBM Research
- Generating Videos with Scene Dynamics
Carl Vondrick*, MIT; Hamed Pirsiavash, ; Antonio Torralba,
- Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs
Daniel Ritchie*, Stanford University; Anna Thomas, Stanford University; Pat Hanrahan, Stanford University; Noah Goodman,
- A Powerful Generative Model Using Random Weights for the Deep Image Representation
Kun He, Huazhong University of Science and Technology; Yan Wang*, HUAZHONG UNIVERSITY OF SCIENCE; John Hopcroft, Cornell University
- Optimizing affinity-based binary hashing using auxiliary coordinates
Ramin Raziperchikolaei, UC Merced; Miguel Carreira-Perpinan*, UC Merced
- Double Thompson Sampling for Dueling Bandits
Huasen Wu*, University of California at Davis; Xin Liu, University of California, Davis
- Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy*, ; Thomas Brox, University of Freiburg
- Dynamic Filter Networks
Xu Jia*, KU Leuven; Bert De Brabandere, ; Tinne Tuytelaars, KU Leuven; Luc Van Gool, ETH Zürich
- A Simple Practical Accelerated Method for Finite Sums
Aaron Defazio*, Ambiata
- Barzilai-Borwein Step Size for Stochastic Gradient Descent
Conghui Tan*, The Chinese University of HK; Shiqian Ma, ; Yu-Hong Dai, ; Yuqiu Qian, The University of Hong Kong
- On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
Guillaume Papa, Télécom ParisTech; Aurélien Bellet*, ; Stephan Clémencon,
- Optimal spectral transportation with application to music transcription
Rémi Flamary, ; Cédric Févotte*, CNRS; Nicolas Courty, ; Valentin Emiya, Aix-Marseille University
- Regularized Nonlinear Acceleration
Damien Scieur*, INRIA - ENS; Alexandre D'Aspremont, ; Francis Bach,
- SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
Dehua Cheng*, Univ. of Southern California; Richard Peng, ; Yan Liu, ; Ioakeim Perros, Georgia Institute of Technology
- Single-Image Depth Perception in the Wild
Weifeng Chen*, University of Michigan; Zhao Fu, University of Michigan; Dawei Yang, University of Michigan; Jia Deng,
- Computational and Statistical Tradeoffs in Learning to Rank
Ashish Khetan*, University of Illinois Urbana-; Sewoong Oh,
- Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Pulkit Agrawal*, UC Berkeley; Ashvin Nair, UC Berkeley; Pieter Abbeel, ; Jitendra Malik, ; Sergey Levine, University of Washington
- Online Convex Optimization with Unconstrained Domains and Losses
Ashok Cutkosky*, Stanford University; Kwabena Boahen, Stanford University
- An ensemble diversity approach to supervised binary hashing
Miguel Carreira-Perpinan*, UC Merced; Ramin Raziperchikolaei, UC Merced
- Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang*, ; Jialei Wang, University of Chicago; Dan Garber, ; Nathan Srebro,
- The Power of Adaptivity in Identifying Statistical Alternatives
Kevin Jamieson*, UC Berkeley; Daniel Haas, ; Ben Recht,
- On Explore-Then-Commit strategies
Aurelien Garivier, ; Tor Lattimore, ; Emilie Kaufmann*,
- Sublinear Time Orthogonal Tensor Decomposition
Zhao Song*, UT-Austin; David Woodruff, ; Huan Zhang, UC-Davis
- DECOrrelated feature space partitioning for distributed sparse regression
Xiangyu Wang*, Duke University; David Dunson, Duke University; Chenlei Leng, University of Warwick
- Deep Alternative Neural Networks: Exploring Contexts as Early as Possible for Action Recognition
Jinzhuo Wang*, PKU; Wenmin Wang, peking university; xiongtao Chen, peking university; Ronggang Wang, peking university; Wen Gao, peking university
- Machine Translation Through Learning From a Communication Game
Di He*, Microsoft; Yingce Xia, USTC; Tao Qin, Microsoft; Liwei Wang, ; Nenghai Yu, USTC; Tie-Yan Liu, Microsoft; wei-Ying Ma, Microsoft
- Dialog-based Language Learning
Jason Weston*,
- Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Theodore Bluche*, A2iA
- Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Hsiang-Fu Yu*, University of Texas at Austin; Nikhil Rao, ; Inderjit Dhillon,
- Active Nearest-Neighbor Learning in Metric Spaces
Aryeh Kontorovich, ; Sivan Sabato*, Ben-Gurion University of the Negev; Ruth Urner, MPI Tuebingen
- Proximal Deep Structured Models
Shenlong Wang*, University of Toronto; Sanja Fidler, ; Raquel Urtasun,
- Faster Projection-free Convex Optimization over the Spectrahedron
Dan Garber*,
- Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
Remi Lam*, MIT; Karen Willcox, MIT; David Wolpert,
- Learning Sound Representations from Unlabeled Video
Yusuf Aytar, MIT; Carl Vondrick*, MIT; Antonio Torralba,
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans*, ; Diederik Kingma,
- Efficient Second Order Online Learning by Sketching
Haipeng Luo*, Princeton University; Alekh Agarwal, Microsoft; Nicolò Cesa-Bianchi, ; John Langford,
- Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis
Yoshinobu Kawahara*, Osaka University
- Distributed Flexible Nonlinear Tensor Factorization
Shandian Zhe*, Purdue University; Kai Zhang, Lawrence Berkeley Lab; Pengyuan Wang, Yahoo! Research; Kuang-chih Lee, ; Zenglin Xu, ; Alan Qi, ; Zoubin Ghahramani,
- The Robustness of Estimator Composition
Pingfan Tang*, University of Utah; Jeff Phillips, University of Utah
- Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats
Bipin Rajendran*, NJIT; Pulkit Tandon, IIT Bombay; Yash Malviya, IIT Bombay
- PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Michael Figurnov*, Skolkovo Inst. of Sc and Tech; Aijan Ibraimova, Skolkovo Institute of Science and Technology; Dmitry P. Vetrov, ; Pushmeet Kohli,
- Differential Privacy without Sensitivity
Kentaro Minami*, The University of Tokyo; HItomi Arai, The University of Tokyo; Issei Sato, The University of Tokyo; Hiroshi Nakagawa,
- Optimal Cluster Recovery in the Labeled Stochastic Block Model
Se-Young Yun*, Los Alamos National Laboratory; Alexandre Proutiere,
- Even Faster SVD Decomposition Yet Without Agonizing Pain
Zeyuan Allen-Zhu*, Princeton University; Yuanzhi Li, Princeton University
- An algorithm for L1 nearest neighbor search via monotonic embedding
Xinan Wang*, UCSD; Sanjoy Dasgupta,
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Junier Oliva, ; Jeff Schneider, CMU; Barnabas Poczos,
- Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
Dan Garber*, ; Ofer Meshi,
- Efficient Nonparametric Smoothness Estimation
Shashank Singh*, Carnegie Mellon University; Simon Du, Carnegie Mellon University; Barnabas Poczos,
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal*, University of Cambridge; Zoubin Ghahramani,
- Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios*, University of Edinburgh; Iain Murray, University of Edinburgh
- Direct Feedback Alignment Provides Learning In Deep Neural Networks
Arild Nøkland*, None
- Safe and Efficient Off-Policy Reinforcement Learning
Remi Munos, Google DeepMind; Thomas Stepleton, Google DeepMind; Anna Harutyunyan, Vrije Universiteit Brussel; Marc Bellemare*, Google DeepMind
- A Multi-Batch L-BFGS Method for Machine Learning
Albert Berahas*, Northwestern University; Jorge Nocedal, Northwestern University; Martin Takac, Lehigh University
- Semiparametric Differential Graph Models
Pan Xu*, University of Virginia; Quanquan Gu, University of Virginia
- Rényi Divergence Variational Inference
Yingzhen Li*, University of Cambridge; Richard E. Turner,
- Doubly Convolutional Neural Networks
Shuangfei Zhai*, Binghamton University; Yu Cheng, IBM Research; Zhongfei Zhang, Binghamton University
- Density Estimation via Discrepancy Based Adaptive Sequential Partition
Dangna Li*, Stanford university; Kun Yang, Google Inc; Wing Wong, Stanford university
- How Deep is the Feature Analysis underlying Rapid Visual Categorization?
Sven Eberhardt*, Brown University; Jonah Cader, Brown University; Thomas Serre,
- Variational Information Maximizing Exploration
Rein Houthooft*, Ghent University - iMinds; UC Berkeley; OpenAI; Xi Chen, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; John Schulman, OpenAI; Filip De Turck, Ghent University - iMinds; Pieter Abbeel,
- Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Timothy Rubin*, Indiana University; Sanmi Koyejo, UIUC; Michael Jones, Indiana University; Tal Yarkoni, University of Texas at Austin
- Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Yexiang Xue*, Cornell University; Zhiyuan Li, Tsinghua University; Stefano Ermon, ; Carla Gomes, Cornell University; Bart Selman,
- Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
Tomoharu Iwata*, ; Makoto Yamada,
- Fast Stochastic Methods for Nonsmooth Nonconvex Optimization
Sashank Jakkam Reddi*, Carnegie Mellon University; Suvrit Sra, MIT; Barnabas Poczos, ; Alexander J. Smola,
- Variance Reduction in Stochastic Gradient Langevin Dynamics
Kumar Dubey*, Carnegie Mellon University; Sashank Jakkam Reddi, Carnegie Mellon University; Sinead Williamson, ; Barnabas Poczos, ; Alexander J. Smola, ; Eric Xing, Carnegie Mellon University
- Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Mehdi Sajjadi*, University of Utah; Mehran Javanmardi, University of Utah; Tolga Tasdizen, University of Utah
- Dense Associative Memory for Pattern Recognition
Dmitry Krotov*, Institute for Advanced Study; John Hopfield, Princeton Neuroscience Institute
- Causal Bandits: Learning Good Interventions via Causal Inference
Finnian Lattimore, Australian National University; Tor Lattimore*, ; Mark Reid,
- Refined Lower Bounds for Adversarial Bandits
Sébastien Gerchinovitz, ; Tor Lattimore*,
- Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Gang Niu*, University of Tokyo; Marthinus du Plessis, ; Tomoya Sakai, ; Yao Ma, ; Masashi Sugiyama, RIKEN / University of Tokyo
- Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$
Yi Xu*, The University of Iowa; Yan Yan, University of Technology Sydney; Qihang Lin, ; Tianbao Yang, University of Iowa
- Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functionals Estimators
Shashank Singh*, Carnegie Mellon University; Barnabas Poczos,
- A state-space model of cross-region dynamic connectivity in MEG/EEG
Ying Yang*, Carnegie Mellon University; Elissa Aminoff, Carnegie Mellon University; Michael Tarr, Carnegie Mellon University; Robert Kass, Carnegie Mellon University
- What Makes Objects Similar: A Unified Multi-Metric Learning Approach
Han-Jia Ye, ; De-Chuan Zhan*, ; Xue-Min Si, Nanjing University; Yuan Jiang, Nanjing University; Zhi-Hua Zhou,
- Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
Nguyen Viet Cuong*, National University of Singapore; Huan Xu, NUS
- Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
Siddartha Ramamohan, Indian Institute of Science; Arun Rajkumar, ; Shivani Agarwal*, Radcliffe Institute, Harvard
- Local Similarity-Aware Deep Feature Embedding
Chen Huang*, Chinese University of HongKong; Chen Change Loy, The Chinese University of HK; Xiaoou Tang, The Chinese University of Hong Kong
- A Communication-Efficient Parallel Algorithm for Decision Tree
Qi Meng*, Peking University; Guolin Ke, Microsoft Research; Taifeng Wang, Microsoft Research; Wei Chen, Microsoft Research; Qiwei Ye, Microsoft Research; Zhi-Ming Ma, Academy of Mathematics and Systems Science, Chinese Academy of Sciences; Tie-Yan Liu, Microsoft Research
- Convex Two-Layer Modeling with Latent Structure
Vignesh Ganapathiraman, University Of Illinois at Chicago; Xinhua Zhang*, UIC; Yaoliang Yu, ; Junfeng Wen, UofA
- Sampling for Bayesian Program Learning
Kevin Ellis*, MIT; Armando Solar-Lezama, MIT; Joshua Tenenbaum,
- Learning Kernels with Random Features
Aman Sinha*, Stanford University; John Duchi,
- Optimal Tagging with Markov Chain Optimization
Nir Rosenfeld*, Hebrew University of Jerusalem; Amir Globerson, Tel Aviv University
- Crowdsourced Clustering: Querying Edges vs Triangles
Ramya Korlakai Vinayak*, Caltech; Hassibi Babak, Caltech
- Mixed vine copulas as joint models of spike counts and local field potentials
Arno Onken*, IIT; Stefano Panzeri, IIT
- Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation
Emmanuel Abbe*, ; Colin Sandon,
- Adaptive Concentration Inequalities for Sequential Decision Problems
Shengjia Zhao*, Tsinghua University; Enze Zhou, Tsinghua University; Ashish Sabharwal, Allen Institute for AI; Stefano Ermon, - Fast mini-batch k-means by nesting
James Newling*, Idiap Research Institute; Francois Fleuret, Idiap Research Institute
- Deep Learning Models of the Retinal Response to Natural Scenes
Lane McIntosh*, Stanford University; Niru Maheswaranathan, Stanford University; Aran Nayebi, Stanford University; Surya Ganguli, Stanford; Stephen Baccus, Stanford University
- Preference Completion from Partial Rankings
Suriya Gunasekar*, UT Austin; Sanmi Koyejo, UIUC; Joydeep Ghosh, UT Austin
- Dynamic Network Surgery for Efficient DNNs
Yiwen Guo*, Intel Labs China; Anbang Yao, ; Yurong Chen,
- Learning a Metric Embedding for Face Recognition using the Multibatch Method
Oren Tadmor, OrCam; Tal Rosenwein, Orcam; Shai Shalev-Shwartz, OrCam; Yonatan Wexler*, OrCam; Amnon Shashua, OrCam
- A Pseudo-Bayesian Algorithm for Robust PCA
Tae-Hyun Oh*, KAIST; David Wipf, ; Yasuyuki Matsushita, Osaka University; In So Kweon, KAIST
- End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Julien Mairal*, Inria
- Stochastic Variance Reduction Methods for Saddle-Point Problems
P. Balamurugan, ; Francis Bach*,
- Flexible Models for Microclustering with Applications to Entity Resolution
Brenda Betancourt, Duke University; Giacomo Zanella, The University of Warick; Jeffrey Miller, Duke University; Hanna Wallach, Microsoft Research; Abbas Zaidi, Duke University; Rebecca C. Steorts*, Duke University
- Catching heuristics are optimal control policies
Boris Belousov*, TU Darmstadt; Gerhard Neumann, ; Constantin Rothkopf, ; Jan Peters,
- Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
Victor Picheny, Institut National de la Recherche Agronomique; Robert Gramacy*, ; Stefan Wild, Argonne National Lab; Sebastien Le Digabel, École Polytechnique de Montréal
- Adaptive Neural Compilation
Rudy Bunel*, Oxford University; Alban Desmaison, Oxford; M. Pawan Kumar, University of Oxford; Pushmeet Kohli, ; Philip Torr,
- Synthesis of MCMC and Belief Propagation
Sung-Soo Ahn*, KAIST; Misha Chertkov, Los Alamos National Laboratory; Jinwoo Shin, KAIST
- Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
Mauro Scanagatta*, Idsia; Giorgio Corani, Idsia; Cassio Polpo de Campos, Queen's University Belfast; Marco Zaffalon, IDSIA
- Unifying Count-Based Exploration and Intrinsic Motivation
Marc Bellemare*, Google DeepMind; Srinivasan Sriram, ; Georg Ostrovski, Google DeepMind; Tom Schaul, ; David Saxton, Google DeepMind; Remi Munos, Google DeepMind
- Large Margin Discriminant Dimensionality Reduction in Prediction Space
Mohammad Saberian*, Netflix; Jose Costa Pereira, UC San Diego; Nuno Nvasconcelos, UC San Diego
- Stochastic Structured Prediction under Bandit Feedback
Artem Sokolov, Heidelberg University; Julia Kreutzer, Heidelberg University; Stefan Riezler*, Heidelberg University
- Simple and Efficient Weighted Minwise Hashing
Anshumali Shrivastava*, Rice University
- Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
Ilija Bogunovic*, EPFL Lausanne; Jonathan Scarlett, ; Andreas Krause, ; Volkan Cevher,
- Structured Sparse Regression via Greedy Hard Thresholding
Prateek Jain, Microsoft Research; Nikhil Rao*, ; Inderjit Dhillon,
- Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Bauer*, University of Cambridge; Mark van der Wilk, University of Cambridge; Carl Rasmussen, University of Cambridge
- SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Elad Richardson*, Technion; Rom Herskovitz, ; Boris Ginsburg, ; Michael Zibulevsky,
- Long-Term Trajectory Planning Using Hierarchical Memory Networks
Stephan Zheng*, Caltech; Yisong Yue, ; Patrick Lucey, Stats
- Learning Tree Structured Potential Games
Vikas Garg*, MIT; Tommi Jaakkola,
- Observational-Interventional Priors for Dose-Response Learning
Ricardo Silva*,
- Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Shahin Jabbari*, University of Pennsylvania; Ryan Rogers, University of Pennsylvania; Aaron Roth, ; Steven Wu, University of Pennsylvania
- Identification and Overidentification of Linear Structural Equation Models
Bryant Chen*, UCLA
- Adaptive Skills Adaptive Partitions (ASAP)
Daniel Mankowitz*, Technion; Timothy Mann, Google DeepMind; Shie Mannor, Technion
- Multiple-Play Bandits in the Position-Based Model
Paul Lagrée*, Université Paris Sud; Claire Vernade, Université Paris Saclay; Olivier Cappe,
- Optimal Black-Box Reductions Between Optimization Objectives
Zeyuan Allen-Zhu*, Princeton University; Elad Hazan,
- On Valid Optimal Assignment Kernels and Applications to Graph Classification
Nils Kriege*, TU Dortmund; Pierre-Louis Giscard, University of York; Richard Wilson, University of York
- Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, ; Seyed-Mohsen Moosavi-Dezfooli*, EPFL; Pascal Frossard, EPFL
- A Non-convex One-Pass Framework for Factorization Machines and Rank-One Matrix Sensing
Ming Lin*, ; Jieping Ye,
- Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
Zeyuan Allen-Zhu*, Princeton University; Yang Yuan, Cornell University; Karthik Sridharan, University of Pennsylvania
- Combinatorial Multi-Armed Bandit with General Reward Functions
Wei Chen*, ; Wei Hu, Princeton University; Fu Li, The University of Texas at Austin; Jian Li, Tsinghua University; Yu Liu, Tsinghua University; Pinyan Lu, Shanghai University of Finance and Economics
- Boosting with Abstention
Corinna Cortes, ; Giulia DeSalvo*, ; Mehryar Mohri,
- Regret of Queueing Bandits
Subhashini Krishnasamy, The University of Texas at Austin; Rajat Sen, The University of Texas at Austin; Ramesh Johari, ; Sanjay Shakkottai*, The University of Texas at Aus
- Deep Learning Games
Dale Schuurmans*, ; Martin Zinkevich, Google
- Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
Antoine Gautier*, Saarland University; Quynh Nguyen, Saarland University; Matthias Hein, Saarland University
- Learning Volumetric 3D Object Reconstruction from Single-View with Projective Transformations
Xinchen Yan*, University of Michigan; Jimei Yang, ; Ersin Yumer, Adobe Research; Yijie Guo, University of Michigan; Honglak Lee, University of Michigan
- A Credit Assignment Compiler for Joint Prediction
Kai-Wei Chang*, ; He He, University of Maryland; Stephane Ross, Google; Hal III, ; John Langford,
- Accelerating Stochastic Composition Optimization
Mengdi Wang*, ; Ji Liu,
- Reward Augmented Maximum Likelihood for Neural Structured Prediction
Mohammad Norouzi*, ; Dale Schuurmans, ; Samy Bengio, ; zhifeng Chen, ; Navdeep Jaitly, ; Mike Schuster, ; Yonghui Wu,
- Consistent Kernel Mean Estimation for Functions of Random Variables
Adam Scibior*, University of Cambridge; Carl-Johann Simon-Gabriel, MPI Tuebingen; Iliya Tolstikhin, ; Bernhard Schoelkopf,
- Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Yizhe Zhang*, Duke university; Xiangyu Wang, Duke University; Changyou Chen, ; Ricardo Henao, ; Kai Fan, Duke university; Lawrence Carin,
- Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher*, ETH Zurich; Brian Mcwilliams, Disney Research; Yannic Kilcher, ETH Zurich; Joachim Buhmann, ETH Zurich; Nicolai Meinshausen,
- Variational Inference in Mixed Probabilistic Submodular Models
Josip Djolonga, ETH Zurich; Sebastian Tschiatschek*, ETH Zurich; Andreas Krause,
- Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Namrata Vaswani*, ; Han Guo, Iowa State University
- The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Barnabas Poczos, ; Jeff Schneider, CMU
- Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Kejun Huang*, University of Minnesota; Xiao Fu, University of Minnesota; Nicholas Sidiropoulos, University of Minnesota
- Bootstrap Model Aggregation for Distributed Statistical Learning
JUN HAN, Dartmouth College; Qiang Liu*,
- A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
Steven Cheng-Xian Li*, UMass Amherst; Benjamin Marlin,
- A Bandit Framework for Strategic Regression
Yang Liu*, Harvard University; Yiling Chen,
- Architectural Complexity Measures of Recurrent Neural Networks
Saizheng Zhang*, University of Montreal; Yuhuai Wu, University of Toronto; Tong Che, IHES; Zhouhan Lin, University of Montreal; Roland Memisevic, University of Montreal; Ruslan Salakhutdinov, University of Toronto; Yoshua Bengio, U. Montreal
- Statistical Inference for Cluster Trees
Jisu Kim*, Carnegie Mellon University; Yen-Chi Chen, Carnegie Mellon University; Sivaraman Balakrishnan, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
- Contextual-MDPs for PAC Reinforcement Learning with Rich Observations
Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; John Langford,
- Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Kihyuk Sohn*,
- Only H is left: Near-tight Episodic PAC RL
Christoph Dann*, Carnegie Mellon University; Emma Brunskill, Carnegie Mellon University - Unsupervised Learning of Spoken Language with Visual Context
David Harwath*, MIT CSAIL; Antonio Torralba, MIT CSAIL; James Glass, MIT CSAIL
- Low-Rank Regression with Tensor Responses
Guillaume Rabusseau*, Aix-Marseille University; Hachem Kadri,
- PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain*, ; Francis Bach, ; Alexandre Lacoste, ; Simon Lacoste-Julien, INRIA
- Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Bo Li*, Vanderbilt University; Yining Wang, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; yevgeniy Vorobeychik, Vanderbilt University
- Learned Region Sparsity and Diversity Also Predicts Visual Attention
Zijun Wei*, Stony Brook; Hossein Adeli, ; Minh Hoai, ; Gregory Zelinsky, ; Dimitris Samaras,
- End-to-End Goal-Driven Web Navigation
Rodrigo Frassetto Nogueira*, New York University; Kyunghyun Cho, University of Montreal
- Automated scalable segmentation of neurons from multispectral images
Uygar Sümbül*, Columbia University; Douglas Roossien, University of Michigan, Ann Arbor; Dawen Cai, University of Michigan, Ann Arbor; John Cunningham, Columbia University; Liam Paninski,
- Privacy Odometers and Filters: Pay-as-you-Go Composition
Ryan Rogers*, University of Pennsylvania; Salil Vadhan, Harvard University; Aaron Roth, ; Jonathan Robert Ullman,
- Minimax Estimation of Maximal Mean Discrepancy with Radial Kernels
Iliya Tolstikhin*, ; Bharath Sriperumbudur, ; Bernhard Schoelkopf,
- Adaptive optimal training of animal behavior
Ji Hyun Bak*, Princeton University; Jung Yoon Choi, ; Ilana Witten, ; Jonathan Pillow,
- Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
Hamidreza Kasaei*, IEETA, University of Aveiro
- Relevant sparse codes with variational information bottleneck
Matthew Chalk*, IST Austria; Olivier Marre, Institut de la vision; Gašper Tkačik, Institute of Science and Technology Austria
- Combinatorial Energy Learning for Image Segmentation
Jeremy Maitin-Shepard*, Google; Viren Jain, Google; Michal Januszewski, Google; Peter Li, ; Pieter Abbeel,
- Orthogonal Random Features
Felix Xinnan Yu*, ; Ananda Theertha Suresh, ; Krzysztof Choromanski, ; Dan Holtmann-Rice, ; Sanjiv Kumar, Google
- Fast Active Set Methods for Online Spike Inference from Calcium Imaging
Johannes Friedrich*, Columbia University; Liam Paninski,
- Diffusion-Convolutional Neural Networks
James Atwood*, UMass Amherst
- Bayesian latent structure discovery from multi-neuron recordings
Scott Linderman*, ; Ryan Adams, ; Jonathan Pillow,
- A Probabilistic Programming Approach To Probabilistic Data Analysis
Feras Saad*, MIT; Vikash Mansinghka, MIT
- A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
William Hoiles*, University of California, Los ; Mihaela Van Der Schaar,
- Inference by Reparameterization in Neural Population Codes
RAJKUMAR VASUDEVA RAJU, Rice University; Xaq Pitkow*,
- Tensor Switching Networks
Chuan-Yung Tsai*, ; Andrew Saxe, ; David Cox,
- Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Alain Durmus, Telecom ParisTech; Umut Simsekli*, ; Eric Moulines, Ecole Polytechnique; Roland Badeau, Telecom ParisTech; Gaël Richard, Telecom ParisTech
- Coordinate-wise Power Method
Qi Lei*, UT AUSTIN; Kai Zhong, UT AUSTIN; Inderjit Dhillon,
- Learning Influence Functions from Incomplete Observations
Xinran He*, USC; Ke Xu, USC; David Kempe, USC; Yan Liu,
- Learning Structured Sparsity in Deep Neural Networks
Wei Wen*, University of Pittsburgh; Chunpeng Wu, University of Pittsburgh; Yandan Wang, University of Pittsburgh; Yiran Chen, University of Pittsburgh; Hai Li, University of Pittsburg
- Sample Complexity of Automated Mechanism Design
Nina Balcan, ; Tuomas Sandholm, Carnegie Mellon University; Ellen Vitercik*, Carnegie Mellon University
- Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
SANGHAMITRA DUTTA*, Carnegie Mellon University; Viveck Cadambe, Pennsylvania State University; Pulkit Grover, Carnegie Mellon University
- Brains on Beats
Umut Güçlü*, Radboud University; Jordy Thielen, Radboud University; Michael Hanke, Otto-von-Guericke University Magdeburg; Marcel Van Gerven, Radboud University
- Learning Transferrable Representations for Unsupervised Domain Adaptation
Ozan Sener*, Cornell University; Hyun Oh Song, Google Research; Ashutosh Saxena, Brain of Things; Silvio Savarese, Stanford University
- Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Stefan Lee*, Indiana University; Senthil Purushwalkam, Carnegie Mellon; Michael Cogswell, Virginia Tech; Viresh Ranjan, Virginia Tech; David Crandall, Indiana University; Dhruv Batra,
- Active Learning from Imperfect Labelers
Songbai Yan*, University of California, San Diego; Kamalika Chaudhuri, University of California, San Diego; Tara Javidi, University of California, San Diego
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob Foerster*, University of Oxford; Yannis Assael, University of Oxford; Nando de Freitas, University of Oxford; Shimon Whiteson,
- Value Iteration Networks
Aviv Tamar*, ; Sergey Levine, ; Pieter Abbeel, ; Yi Wu, UC Berkeley; Garrett Thomas, UC Berkeley
- Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
Dogyoon Song*, MIT; Christina Lee, MIT; Yihua Li, MIT; Devavrat Shah,
- On the Recursive Teaching Dimension of VC Classes
Bo Tang*, University of Oxford; Xi Chen, Columbia University; Yu Cheng, U of Southern California
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen*, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; Rein Houthooft, Ghent University - iMinds; UC Berkeley; OpenAI; John Schulman, OpenAI; Ilya Sutskever, ; Pieter Abbeel,
- Hardness of Online Sleeping Combinatorial Optimization Problems
Satyen Kale*, ; Chansoo Lee, ; David Pal,
- Mixed Linear Regression with Multiple Components
Kai Zhong*, UT AUSTIN; Prateek Jain, Microsoft Research; Inderjit Dhillon,
- Sequential Neural Models with Stochastic Layers
Marco Fraccaro*, DTU; Søren Sønderby, KU; Ulrich Paquet, ; Ole Winther, DTU
- Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Hongseok Namkoong*, Stanford University; John Duchi,
- Minimizing Quadratic Functions in Constant Time
Kohei Hayashi*, AIST; Yuichi Yoshida, NII
- Improved Techniques for Training GANs
Tim Salimans*, ; Ian Goodfellow, OpenAI; Wojciech Zaremba, OpenAI; Vicki Cheung, OpenAI; Alec Radford, OpenAI; Xi Chen, UC Berkeley; OpenAI
- DeepMath - Deep Sequence Models for Premise Selection
Geoffrey Irving*, ; Christian Szegedy, ; Alexander Alemi, Google; Francois Chollet, ; Josef Urban, Czech Technical University in Prague
- Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar, NYU; Arthur Szlam, ; Rob Fergus*, New York University - Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely*, ; Roy Frostig, Stanford University; Yoram Singer, Google
- Learning the Number of Neurons in Deep Networks
Jose Alvarez*, NICTA; Mathieu Salzmann, EPFL
- Finding significant combinations of features in the presence of categorical covariates
Laetitia Papaxanthos*, ETH Zurich; Felipe Llinares, ETH Zurich; Dean Bodenham, ETH Zurich; Karsten Borgwardt,
- Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning
Been Kim*, ; Rajiv Khanna, UT Austin; Sanmi Koyejo, UIUC
- Optimistic Bandit Convex Optimization
Scott Yang*, New York University; Mehryar Mohri,
- Safe Policy Improvement by Minimizing Robust Baseline Regret
Mohamad Ghavamzadeh*, ; Marek Petrik, ; Yinlam Chow, Stanford University
- Graphons, mergeons, and so on!
Justin Eldridge*, The Ohio State University; Mikhail Belkin, ; Yusu Wang, The Ohio State University
- Hierarchical Clustering via Spreading Metrics
Aurko Roy*, Georgia Tech; Sebastian Pokutta, GeorgiaTech
- Learning Bayesian networks with ancestral constraints
Eunice Yuh-Jie Chen*, UCLA; Yujia Shen, ; Arthur Choi, ; Adnan Darwiche,
- Pruning Random Forests for Prediction on a Budget
Feng Nan*, Boston University; Joseph Wang, Boston University; Venkatesh Saligrama,
- Clustering with Bregman Divergences: an Asymptotic Analysis
Chaoyue Liu*, The Ohio State University; Mikhail Belkin,
- Variational Autoencoder for Deep Learning of Images, Labels and Captions
Yunchen Pu*, Duke University; Zhe Gan, Duke; Ricardo Henao, ; Xin Yuan, Bell Labs; chunyuan Li, Duke; Andrew Stevens, Duke University; Lawrence Carin,
- Encode, Review, and Decode: Reviewer Module for Caption Generation
Zhilin Yang*, Carnegie Mellon University; Ye Yuan, Carnegie Mellon University; Yuexin Wu, Carnegie Mellon University; William Cohen, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto
- Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu*, ; Dilin Wang, Dartmouth College
- A Bio-inspired Redundant Sensing Architecture
Anh Tuan Nguyen*, University of Minnesota; Jian Xu, University of Minnesota; Zhi Yang, University of Minnesota
- Contextual semibandits via supervised learning oracles
Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; Miro Dudik,
- Blind Attacks on Machine Learners
Alex Beatson*, Princeton University; Zhaoran Wang, Princeton University; Han Liu,
- Universal Correspondence Network
Christopher Choy*, Stanford University; Manmohan Chandraker, NEC Labs America; JunYoung Gwak, Stanford University; Silvio Savarese, Stanford University
- Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh*, UC Davis; Andy Cotter, ; Maya Gupta, ; Michael Friedlander, UC Davis
- Deep Learning for Predicting Human Strategic Behavior
Jason Hartford*, University of British Columbia; Kevin Leyton-Brown, ; James Wright, University of British Columbia
- Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Sougata Chaudhuri*, University of Michigan ; Ambuj Tewari, University of Michigan
- Eliciting and Aggregating Categorical Data
Yiling Chen, ; Rafael Frongillo, ; Chien-Ju Ho*,
- Measuring the reliability of MCMC inference with Bidirectional Monte Carlo
Roger Grosse, ; Siddharth Ancha, University of Toronto; Daniel Roy*,
- Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Weihao Gao, UIUC; Sewoong Oh*, ; Pramod Viswanath, UIUC
- Selective inference for group-sparse linear models
Fan Yang, University of Chicago; Rina Foygel Barber*, ; Prateek Jain, Microsoft Research; John Lafferty,
- Graph Clustering: Block-models and model free results
Yali Wan*, University of Washington; Marina Meila, University of Washington
- Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
Christopher Lynn*, University of Pennsylvania; Dan Lee , University of Pennsylvania
- Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Neuroscience
Hao Zhou, University of Wisconsin Madiso; Vamsi Ithapu*, University of Wisconsin Madison; Sathya Ravi, University of Wisconsin Madiso; Vikas Singh, UW Madison; Grace Wahba, University of Wisconsin Madison; Sterling Johnson, University of Wisconsin Madison
- Geometric Dirichlet Means Algorithm for Topic Inference
Mikhail Yurochkin*, University of Michigan; Long Nguyen,
- Structured Prediction Theory Based on Factor Graph Complexity
Corinna Cortes, ; Vitaly Kuznetsov*, Courant Institute; Mehryar Mohri, ; Scott Yang, New York University
- Improved Dropout for Shallow and Deep Learning
Zhe Li, The University of Iowa; Boqing Gong, University of Central Florida; Tianbao Yang*, University of Iowa
- Constraints Based Convex Belief Propagation
Yaniv Tenzer*, The Hebrew University; Alexander Schwing, ; Kevin Gimpel, ; Tamir Hazan,
- Error Analysis of Generalized Nyström Kernel Regression
Hong Chen, University of Texas; Haifeng Xia, Huazhong Agricultural University; Heng Huang*, University of Texas Arlington
- A Probabilistic Framework for Deep Learning
Ankit Patel, Baylor College of Medicine; Rice University; Tan Nguyen*, Rice University; Richard Baraniuk,
- General Tensor Spectral Co-clustering for Higher-Order Data
Tao Wu*, Purdue University; Austin Benson, Stanford University; David Gleich,
- Cyclades: Conflict-free Asynchronous Machine Learning
Xinghao Pan*, UC Berkeley; Stephen Tu, UC Berkeley; Maximilian Lam, UC Berkeley; Dimitris Papailiopoulos, ; Ce Zhang, Stanford; Michael Jordan, ; Kannan Ramchandran, ; Christopher Re, ; Ben Recht,
- Single Pass PCA of Matrix Products
Shanshan Wu*, UT Austin; Srinadh Bhojanapalli, TTI Chicago; Sujay Sanghavi, ; Alexandros G. Dimakis,
- Stochastic Variational Deep Kernel Learning
Andrew Wilson*, Carnegie Mellon University; Zhiting Hu, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto; Eric Xing, Carnegie Mellon University
- Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
Marc Vuffray*, Los Alamos National Laboratory; Sidhant Misra, Los Alamos National Laboratory; Andrey Lokhov, Los Alamos National Laboratory; Misha Chertkov, Los Alamos National Laboratory
- Long-term Causal Effects via Behavioral Game Theory
Panos Toulis*, University of Chicago; David Parkes, Harvard University
- Measuring Neural Net Robustness with Constraints
Osbert Bastani*, Stanford University; Yani Ioannou, University of Cambridge; Leonidas Lampropoulos, University of Pennsylvania; Dimitrios Vytiniotis, Microsoft Research; Aditya Nori, Microsoft Research; Antonio Criminisi,
- Reshaped Wirtinger Flow for Solving Quadratic Systems of Equations
Huishuai Zhang*, Syracuse University; Yingbin Liang, Syracuse University
- Nearly Isometric Embedding by Relaxation
James McQueen*, University of Washington; Marina Meila, University of Washington; Dominique Joncas, Google
- Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
Kevin Winner*, UMass CICS; Daniel Sheldon,
- Causal meets Submodular: Subset Selection with Directed Information
Yuxun Zhou*, UC Berkeley; Costas Spanos,
- Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Ayan Chakrabarti*, ; Jingyu Shao, UCLA; Greg Shakhnarovich,
- Deep Neural Networks with Inexact Matching for Person Re-Identification
Arulkumar Subramaniam, IIT Madras; Moitreya Chatterjee*, IIT Madras; Anurag Mittal, IIT Madras
- Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
Ji Xu, Columbia university; Daniel Hsu*, ; Arian Maleki, Columbia University
- Estimating the class prior and posterior from noisy positives and unlabeled data
Shanatnu Jain*, Indiana University; Martha White, ; Predrag Radivojac,
- Kronecker Determinantal Point Processes
Zelda Mariet*, MIT; Suvrit Sra, MIT
- Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
Lalit Jain*, University of Wisconsin-Madison; Kevin Jamieson, UC Berkeley; Robert Nowak, University of Wisconsin Madison
- Feature-distributed sparse regression: a screen-and-clean approach
Jiyan Yang*, Stanford University; Michael Mahoney, ; Michael Saunders, Stanford University; Yuekai Sun, University of Michigan
- Learning Bound for Parameter Transfer Learning
Wataru Kumagai*, Kanagawa University
- Learning under uncertainty: a comparison between R-W and Bayesian approach
He Huang*, LIBR; Martin Paulus, LIBR
- Bi-Objective Online Matching and Submodular Allocations
Hossein Esfandiari*, University of Maryland; Nitish Korula, Google Research; Vahab Mirrokni, Google
- Quantized Random Projections and Non-Linear Estimation of Cosine Similarity
Ping Li, ; Michael Mitzenmacher, Harvard University; Martin Slawski*,
- The non-convex Burer-Monteiro approach works on smooth semidefinite programs
Nicolas Boumal, ; Vlad Voroninski*, MIT; Afonso Bandeira,
- Dimensionality Reduction of Massive Sparse Datasets Using Coresets
Dan Feldman, ; Mikhail Volkov*, MIT; Daniela Rus, MIT
- Using Social Dynamics to Make Individual Predictions: Variational Inference with Stochastic Kinetic Model
Zhen Xu*, SUNY at Buffalo; Wen Dong, ; Sargur Srihari,
- Supervised learning through the lens of compression
Ofir David*, Technion - Israel institute of technology; Shay Moran, Technion - Israel institue of Technology; Amir Yehudayoff, Technion - Israel institue of Technology
- Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Xinghua Lou*, Vicarious FPC Inc; Ken Kansky, ; Wolfgang Lehrach, ; CC Laan, ; Bhaskara Marthi, ; D. Scott Phoenix, ; Dileep George,
- Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Xiao-Jiao Mao, Nanjing University; Chunhua Shen*, ; Yu-Bin Yang,
- Object based Scene Representations using Fisher Scores of Local Subspace Projections
Mandar Dixit*, UC San Diego; Nuno Vasconcelos,
- Active Learning with Oracle Epiphany
Tzu-Kuo Huang, Microsoft Research; Lihong Li, Microsoft Research; Ara Vartanian, University of Wisconsin-Madison; Saleema Amershi, Microsoft; Xiaojin Zhu*,
- Statistical Inference for Pairwise Graphical Models Using Score Matching
Ming Yu*, The University of Chicago; Mladen Kolar, ; Varun Gupta, University of Chicago
- Improved Error Bounds for Tree Representations of Metric Spaces
Samir Chowdhury*, The Ohio State University; Facundo Memoli, ; Zane Smith,
- Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Arturo Deza*, UCSB; Miguel Eckstein, UCSB
- On Multiplicative Integration with Recurrent Neural Networks
Yuhuai Wu*, University of Toronto; Saizheng Zhang, University of Montreal; ying Zhang, University of Montreal; Yoshua Bengio, U. Montreal; Ruslan Salakhutdinov, University of Toronto
- Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Kirthevasan Kandasamy*, CMU; Maruan Al-Shedivat, CMU; Eric Xing, Carnegie Mellon University
- Regret Bounds for Non-decomposable Metrics with Missing Labels
Nagarajan Natarajan*, Microsoft Research Bangalore; Prateek Jain, Microsoft Research
- Robust k-means: a Theoretical Revisit
ALEXANDROS GEORGOGIANNIS*, TECHNICAL UNIVERSITY OF CRETE
- Bayesian optimization for automated model selection
Gustavo Malkomes, Washington University; Charles Schaff, Washington University in St. Louis; Roman Garnett*,
- A Probabilistic Model of Social Decision Making based on Reward Maximization
Koosha Khalvati*, University of Washington; Seongmin Park, Cognitive Neuroscience Center; Jean-Claude Dreher, Centre de Neurosciences Cognitives; Rajesh Rao, University of Washington
- Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Ahmed Alaa*, UCLA; Mihaela Van Der Schaar,
- Fast and Flexible Monotonic Functions with Ensembles of Lattices
Mahdi Fard, ; Kevin Canini, ; Andy Cotter, ; Jan Pfeifer, Google; Maya Gupta*,
- Conditional Generative Moment-Matching Networks
Yong Ren, Tsinghua University; Jun Zhu*, ; Jialian Li, Tsinghua University; Yucen Luo,
- Stochastic Gradient MCMC with Stale Gradients
Changyou Chen*, ; Nan Ding, Google; chunyuan Li, Duke; Yizhe Zhang, Duke university; Lawrence Carin,
- Composing graphical models with neural networks for structured representations and fast inference
Matthew Johnson, ; David Duvenaud*, ; Alex Wiltschko, Harvard University and Twitter; Ryan Adams, ; Sandeep Datta, Harvard Medical School
- Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling
Nina Balcan, ; Hongyang Zhang*, CMU
- Combinatorial semi-bandit with known covariance
Rémy Degenne*, Université Paris Diderot; Vianney Perchet,
- Matrix Completion has No Spurious Local Minimum
Rong Ge, ; Jason Lee, UC Berkeley; Tengyu Ma*, Princeton University
- The Multiscale Laplacian Graph Kernel
Risi Kondor*, ; Horace Pan, UChicago
- Adaptive Averaging in Accelerated Descent Dynamics
Walid Krichene*, UC Berkeley; Alexandre Bayen, UC Berkeley; Peter Bartlett,
- Sub-sampled Newton Methods with Non-uniform Sampling
Peng Xu*, Stanford University; Jiyan Yang, Stanford University; Farbod Roosta-Khorasani, University of California Berkeley; Christopher Re, ; Michael Mahoney,
- Stochastic Gradient Geodesic MCMC Methods
Chang Liu*, Tsinghua University; Jun Zhu, ; Yang Song, Stanford University
- Variational Bayes on Monte Carlo Steroids
Aditya Grover*, Stanford University; Stefano Ermon,
- Showing versus doing: Teaching by demonstration
Mark Ho*, Brown University; Michael L. Littman, ; James MacGlashan, Brown University; Fiery Cushman, Harvard University; Joe Austerweil,
- Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Jianxu Chen*, University of Notre Dame; Lin Yang, University of Notre Dame; Yizhe Zhang, University of Notre Dame; Mark Alber, University of Notre Dame; Danny Chen, University of Notre Dame
- Maximization of Approximately Submodular Functions
Thibaut Horel*, Harvard University; Yaron Singer,
- A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
Xiangru Lian, University of Rochester; Huan Zhang, ; Cho-Jui Hsieh, ; Yijun Huang, ; Ji Liu*,
- Learning Infinite RBMs with Frank-Wolfe
Wei Ping*, UC Irvine; Qiang Liu, ; Alexander Ihler,
- Estimating the Size of a Large Network and its Communities from a Random Sample
Lin Chen*, Yale University; Amin Karbasi, ; Forrest Crawford, Yale University
- Learning Sensor Multiplexing Design through Back-propagation
Ayan Chakrabarti*,
- On Robustness of Kernel Clustering
Bowei Yan*, University of Texas at Austin; Purnamrita Sarkar, U.C. Berkeley
- High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
Kameron Harris*, University of Washington; Stefan Mihalas, Allen Institute for Brain Science; Eric Shea-Brown, University of Washington
- MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
Gregory Rogez*, Inria; Cordelia Schmid,
- A New Liftable Class for First-Order Probabilistic Inference
Seyed Mehran Kazemi*, UBC; Angelika Kimmig, KU Leuven; Guy Van den Broeck, ; David Poole, UBC
- The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Jian Wu*, Cornell University; Peter I. Frazier,
- Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Vasilis Syrgkanis*, ; Haipeng Luo, Princeton University; Akshay Krishnamurthy, ; Robert Schapire,
- Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
Ilya Shpitser*,
- Optimistic Gittins Indices
Eli Gutin*, Massachusetts Institute of Tec; Vivek Farias,
- Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
Juho Lee*, POSTECH; Lancelot James, HKUST; Seungjin Choi, POSTECH
- Launch and Iterate: Reducing Prediction Churn
Mahdi Fard, ; Quentin Cormier, Google; Kevin Canini, ; Maya Gupta*,
- “Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
Wen-Hao Zhang*, Institute of Neuroscience, Chinese Academy of Sciences; He Wang, HKUST; K. Y. Michael Wong, HKUST; Si Wu,
- Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini*, University of Lugano; Jonathan Masci, ; Emanuele Rodolà, University of Lugano; Michael Bronstein, University of Lugano
- Pairwise Choice Markov Chains
Stephen Ragain*, Stanford University; Johan Ugander,
- NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Davood Hajinezhad*, Iowa State University; Mingyi Hong, ; Tuo Zhao, Johns Hopkins University; Zhaoran Wang, Princeton University
- Clustering with Same-Cluster Queries
Hassan Ashtiani, University of Waterloo; Shrinu Kushagra*, University of Waterloo; Shai Ben-David, U. Waterloo
- Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami*, Google DeepMind; Nicolas Heess, ; Theophane Weber, ; Yuval Tassa, Google DeepMind; David Szepesvari, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Geoffrey Hinton, Google
- Parameter Learning for Log-supermodular Distributions
Tatiana Shpakova*, Inria - ENS Paris; Francis Bach,
- Deconvolving Feedback Loops in Recommender Systems
Ayan Sinha*, Purdue; David Gleich, ; Karthik Ramani, Purdue University
- Structured Matrix Recovery via the Generalized Dantzig Selector
Sheng Chen*, University of Minnesota; Arindam Banerjee,
- Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
Himabindu Lakkaraju*, Stanford University; Jure Leskovec,
- Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Noah Apthorpe*, Princeton University; Alexander Riordan, Princeton University; Robert Aguilar, Princeton University; Jan Homann, Princeton University; Yi Gu, Princeton University; David Tank, Princeton University; H. Sebastian Seung, Princeton University
- Designing smoothing functions for improved worst-case competitive ratio in online optimization
Reza Eghbali*, University of washington; Maryam Fazel, University of Washington
- Convergence guarantees for kernel-based quadrature rules in misspecified settings
Motonobu Kanagawa*, ; Bharath Sriperumbudur, ; Kenji Fukumizu,
- Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Bo Wang*, Stanford University; Junjie Zhu, Stanford University; Armin Pourshafeie, Stanford University
- A non-generative framework and convex relaxations for unsupervised learning
Elad Hazan, ; Tengyu Ma*, Princeton University
- Equality of Opportunity in Supervised Learning
Moritz Hardt*, ; Eric Price, ; Nathan Srebro,
- Scaled Least Squares Estimator for GLMs in Large-Scale Problems
Murat Erdogdu*, Stanford University; Lee Dicker, ; Mohsen Bayati,
- Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Yuan Zhao*, Stony Brook University; Il Memming Park,
- Search Improves Label for Active Learning
Alina Beygelzimer, Yahoo Inc; Daniel Hsu, ; John Langford, ; Chicheng Zhang*, UCSD
- Higher-Order Factorization Machines
Mathieu Blondel*, NTT; Akinori Fujino, NTT; Naonori Ueda, ; Masakazu Ishihata, Hokkaido University
- Exponential expressivity in deep neural networks through transient chaos
Ben Poole*, Stanford University; Subhaneil Lahiri, Stanford University; Maithra Raghu, Cornell University; Jascha Sohl-Dickstein, ; Surya Ganguli, Stanford
- Split LBI: An Iterative Regularization Path with Structural Sparsity
Chendi Huang, Peking University; Xinwei Sun, ; Jiechao Xiong, Peking University; Yuan Yao*,
- An equivalence between high dimensional Bayes optimal inference and M-estimation
Madhu Advani*, Stanford University; Surya Ganguli, Stanford
- Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen*, University of Wyoming; Alexey Dosovitskiy, ; Jason Yosinski, Cornell; Thomas Brox, University of Freiburg; Jeff Clune,
- Deep Submodular Functions
Brian Dolhansky*, University of Washington; Jeff Bilmes, University of Washington, Seattle
- Discriminative Gaifman Models
Mathias Niepert*,
- Leveraging Sparsity for Efficient Submodular Data Summarization
Erik Lindgren*, University of Texas at Austin; Shanshan Wu, UT Austin; Alexandros G. Dimakis,
- Local Minimax Complexity of Stochastic Convex Optimization
Sabyasachi Chatterjee, University of Chicago; John Duchi, ; John Lafferty, ; Yuancheng Zhu*, University of Chicago
- Stochastic Optimization for Large-scale Optimal Transport
Aude Genevay*, Université Paris Dauphine; Marco Cuturi, ; Gabriel Peyré, ; Francis Bach,
- On Mixtures of Markov Chains
Rishi Gupta*, Stanford; Ravi Kumar, ; Sergei Vassilvitskii, Google
- Linear Contextual Bandits with Knapsacks
Shipra Agrawal*, ; Nikhil Devanur, Microsoft Research
- Reconstructing Parameters of Spreading Models from Partial Observations
Andrey Lokhov*, Los Alamos National Laboratory
- Spatiotemporal Residual Networksfor Video Action Recognition
Christoph Feichtenhofer*, Graz University of Technology; Axel Pinz, Graz University of Technology; Richard Wildes, York University Toronto
- Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
Behnam Neyshabur*, TTI-Chicago; Yuhuai Wu, University of Toronto; Ruslan Salakhutdinov, University of Toronto; Nathan Srebro,
- Strategic Attentive Writer for Learning Macro-Actions
Alexander Vezhnevets*, Google DeepMind; Volodymyr Mnih, ; Simon Osindero, Google DeepMind; Alex Graves, ; Oriol Vinyals, ; John Agapiou, ; Koray Kavukcuoglu, Google DeepMind
- The Limits of Learning with Missing Data
Brian Bullins*, Princeton University; Elad Hazan, ; Tomer Koren, Technion---Israel Inst. of Technology
- RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism
Edward Choi*, Georgia Institute of Technolog; Mohammad Taha Bahadori, Gatech; Jimeng Sun,
- Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
Yu-Xiang Wang*, Carnegie Mellon University; Veeranjaneyulu Sadhanala, Carnegie Mellon University; Ryan Tibshirani,
- Community Detection on Evolving Graphs
Stefano Leonardi*, Sapienza University of Rome; Aris Anagnostopoulos, Sapienza University of Rome; Jakub Łącki, Sapienza University of Rome; Silvio Lattanzi, Google; Mohammad Mahdian, Google Research, New York
- Online and Differentially-Private Tensor Decomposition
Yining Wang*, Carnegie Mellon University; Anima Anandkumar, UC Irvine
- Dimension-Free Iteration Complexity of Finite Sum Optimization Problems
Yossi Arjevani*, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of Science
- Towards Conceptual Compression
Karol Gregor*, ; Frederic Besse, Google DeepMind; Danilo Jimenez Rezende, ; Ivo Danihelka, ; Daan Wierstra, Google DeepMind
- Exact Recovery of Hard Thresholding Pursuit
Xiaotong Yuan*, Nanjing University of Informat; Ping Li, ; Tong Zhang,
- Data Programming: Creating Large Training Sets, Quickly
Alexander Ratner*, Stanford University; Christopher De Sa, Stanford University; Sen Wu, Stanford University; Daniel Selsam, Stanford; Christopher Ré, Stanford University
- Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
Vitaly Feldman*,
- Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Liangbei Xu*, Gatech; Mark Davenport,
- Fast Distributed Submodular Cover: Public-Private Data Summarization
Baharan Mirzasoleiman*, ETH Zurich; Morteza Zadimoghaddam, ; Amin Karbasi,
- Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods
Cristina Savin*, IST Austria; Gašper Tkačik, Institute of Science and Technology Austria
- Lifelong Learning with Weighted Majority Votes
Anastasia Pentina*, IST Austria; Ruth Urner, MPI Tuebingen
- Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack Rae*, Google DeepMind; Jonathan Hunt, ; Ivo Danihelka, ; Tim Harley, Google DeepMind; Andrew Senior, ; Greg Wayne, ; Alex Graves, ; Timothy Lillicrap, Google DeepMind
- Matching Networks for One Shot Learning
Oriol Vinyals*, ; Charles Blundell, DeepMind; Timothy Lillicrap, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Daan Wierstra, Google DeepMind
- Tight Complexity Bounds for Optimizing Composite Objectives
Blake Woodworth*, Toyota Technological Institute; Nathan Srebro,
- Graphical Time Warping for Joint Alignment of Multiple Curves
Yizhi Wang, Virginia Tech; David Miller, The Pennsylvania State University; Kira Poskanzer, University of California, San Francisco; Yue Wang, Virginia Tech; Lin Tian, The University of California, Davis; Guoqiang Yu*,
- Unsupervised Risk Estimation Using Only Conditional Independence Structure
Jacob Steinhardt*, Stanford University; Percy Liang,
- MetaGrad: Multiple Learning Rates in Online Learning
Tim Van Erven*, ; Wouter M. Koolen,
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas Kulkarni, MIT; Karthik Narasimhan*, MIT; Ardavan Saeedi, MIT; Joshua Tenenbaum,
- High Dimensional Structured Superposition Models
Qilong Gu*, University of Minnesota; Arindam Banerjee,
- Joint quantile regression in vector-valued RKHSs
Maxime Sangnier*, LTCI, CNRS, Télécom ParisTech; Olivier Fercoq, ; Florence d’Alché-Buc,
- The Forget-me-not Process
Kieran Milan, Google DeepMind; Joel Veness*, ; James Kirkpatrick, Google DeepMind; Michael Bowling, ; Anna Koop, University of Alberta; Demis Hassabis,
- Wasserstein Training of Restricted Boltzmann Machines
Gregoire Montavon*, ; Klaus-Robert Muller, ; Marco Cuturi,
- Communication-Optimal Distributed Clustering
Jiecao Chen, Indiana University Bloomington; He Sun*, The University of Bristol; David Woodruff, ; Qin Zhang,
- Probing the Compositionality of Intuitive Functions
Eric Schulz*, University College London; Joshua Tenenbaum, ; David Duvenaud, ; Maarten Speekenbrink, University College London; Sam Gershman,
- Ladder Variational Autoencoders
Casper Kaae Sønderby*, University of Copenhagen; Tapani Raiko, ; Lars Maaløe, Technical University of Denmark; Søren Sønderby, KU; Ole Winther, Technical University of Denmark
- The Multiple Quantile Graphical Model
Alnur Ali*, Carnegie Mellon University; Zico Kolter, ; Ryan Tibshirani,
- Threshold Learning for Optimal Decision Making
Nathan Lepora*, University of Bristol
- Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvärinen*, ; Hiroshi Morioka, University of Helsinki
- Can Active Memory Replace Attention?
Łukasz Kaiser*, ; Samy Bengio,
- Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Taiji Suzuki*, ; Heishiro Kanagawa, ; Hayato Kobayashi, ; Nobuyuki Shimizu, ; Yukihiro Tagami,
- The Product Cut
Thomas Laurent*, Loyola Marymount University; James Von Brecht, CSULB; Xavier Bresson, ; Arthur Szlam,
- Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Mohammad Javad Hosseini*, University of Washington; Su-In Lee,
- Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
Firas Abuzaid*, MIT; Joseph Bradley, Databricks; Feynman Liang, Cambridge University Engineering Department; Andrew Feng, Yahoo!; Lee Yang, Yahoo!; Matei Zaharia, MIT; Ameet Talwalkar,
- Average-case hardness of RIP certification
Tengyao Wang, University of Cambridge; Quentin Berthet*, ; Yaniv Plan, University of British Columbia
- Forward models at Purkinje synapses facilitate cerebellar anticipatory control
Ivan Herreros-Alonso*, Universitat Pompeu Fabra; Xerxes Arsiwalla, ; Paul Verschure,
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard*, EPFL; Xavier Bresson, ; pierre Vandergheynst, EPFL
- Deep Unsupervised Exemplar Learning
MIGUEL BAUTISTA*, HEIDELBERG UNIVERSITY; Artsiom Sanakoyeu, Heidelberg University; Ekaterina Tikhoncheva, Heidelberg University; Björn Ommer,
- Large-Scale Price Optimization via Network Flow
Shinji Ito*, NEC Coorporation; Ryohei Fujimaki,
- Online Pricing with Strategic and Patient Buyers
Michal Feldman, TAU; Tomer Koren, Technion---Israel Inst. of Technology; Roi Livni*, Huji; Yishay Mansour, Microsoft; Aviv Zohar, huji
- Global Optimality of Local Search for Low Rank Matrix Recovery
Srinadh Bhojanapalli*, TTI Chicago; Behnam Neyshabur, TTI-Chicago; Nathan Srebro,
- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Daniel Neil*, Institute of Neuroinformatics; Michael Pfeiffer, Institute of Neuroinformatics; Shih-Chii Liu,
- Improving PAC Exploration Using the Median of Means
Jason Pazis*, MIT; Ronald Parr, ; Jonathan How, MIT
- Infinite Hidden Semi-Markov Modulated Interaction Point Process
Matt Zhang*, Nicta; Peng Lin, Data61; Ting Guo, Data61; Yang Wang, Data61, CSIRO; Fang Chen, Data61, CSIRO
- Cooperative Inverse Reinforcement Learning
Dylan Hadfield-Menell*, UC Berkeley; Stuart Russell, UC Berkeley; Pieter Abbeel, ; Anca Dragan,
- Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
Ransalu Senanayake*, The University of Sydney; Lionel Ott, The University of Sydney; Simon O'Callaghan, NICTA; Fabio Ramos, The University of Sydney
- Select-and-Sample for Spike-and-Slab Sparse Coding
Abdul-Saboor Sheikh, University of Oldenburg; Jörg Lücke*,
- Tractable Operations for Arithmetic Circuits of Probabilistic Models
Yujia Shen*, ; Arthur Choi, ; Adnan Darwiche,
- Greedy Feature Construction
Dino Oglic*, University of Bonn; Thomas Gaertner, The University of Nottingham
- Mistake Bounds for Binary Matrix Completion
Mark Herbster, ; Stephen Pasteris, UCL; Massimiliano Pontil*,
- Data driven estimation of Laplace-Beltrami operator
Frederic Chazal, INRIA; Ilaria Giulini, ; Bertrand Michel*,
- Tracking the Best Expert in Non-stationary Stochastic Environments
Chen-Yu Wei*, Academia Sinica; Yi-Te Hong, Academia Sinica; Chi-Jen Lu, Academia Sinica
- Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz*, Google Deepmind; Misha Denil, ; Sergio Gomez, Google DeepMind; Matthew Hoffman, Google DeepMind; David Pfau, Google DeepMind; Tom Schaul, ; Nando Freitas, Google
- Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
Hassan Kingravi, Pindrop Security, Harshal Maske, UIUC, Girish Chowdhary*, UIUC
- Quantum Perceptron Models
Ashish Kapoor*, ; Nathan Wiebe, Microsoft Research; Krysta M. Svore,
- Guided Policy Search as Approximate Mirror Descent
William Montgomery*, University of Washington; Sergey Levine, University of Washington
- The Power of Optimization from Samples
Eric Balkanski*, Harvard University; Aviad Rubinstein, UC Berkeley; Yaron Singer,
- Deep Exploration via Bootstrapped DQN
Ian Osband*, DeepMind; Charles Blundell, DeepMind; Alexander Pritzel, ; Benjamin Van Roy,
- A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
Jingwei Liang*, GREYC, ENSICAEN; Jalal Fadili, ; Gabriel Peyré,
- Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Yin Cheng Ng*, University College London; Pawel Chilinski, University College London; Ricardo Silva, University College London
- Convolutional Neural Fabrics
Shreyas Saxena*, INRIA; Jakob Verbeek,
- A Neural Transducer
Navdeep Jaitly*, ; Quoc Le, ; Oriol Vinyals, ; Ilya Sutskever, ; David Sussillo, Google; Samy Bengio,
- Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
Aryan Mokhtari*, University of Pennsylvania; Hadi Daneshmand, ETH Zurich; Aurelien Lucchi, ; Thomas Hofmann, ; Alejandro Ribeiro, University of Pennsylvania
- A Sparse Interactive Model for Inductive Matrix Completion
Jin Lu, University of Connecticut; Guannan Liang, University of Connecticut; jiangwen Sun, University of Connecticut; Jinbo Bi*, University of Connecticut
- Coresets for Scalable Bayesian Logistic Regression
Jonathan Huggins*, MIT; Trevor Campbell, MIT; Tamara Broderick, MIT
- Agnostic Estimation for Misspecified Phase Retrieval Models
Matey Neykov*, Princeton University; Zhaoran Wang, Princeton University; Han Liu,
- Linear Relaxations for Finding Diverse Elements in Metric Spaces
Aditya Bhaskara*, University of Utah; Mehrdad Ghadiri, Sharif University of Technolog; Vahab Mirrokni, Google; Ola Svensson, EPFL
- Binarized Neural Networks
Itay Hubara*, Technion; Matthieu Courbariaux, Université de Montréal; Daniel Soudry, Columbia University; Ran El-Yaniv, Technion; Yoshua Bengio, Université de Montréal
- On Local Maxima in the Population Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Chi Jin*, UC Berkeley; Yuchen Zhang, ; Sivaraman Balakrishnan, CMU; Martin Wainwright, UC Berkeley; Michael Jordan,
- Memory-Efficient Backpropagation Through Time
Audrunas Gruslys*, Google DeepMind; Remi Munos, Google DeepMind; Ivo Danihelka, ; Marc Lanctot, Google DeepMind; Alex Graves,
- Bayesian Optimization with Robust Bayesian Neural Networks
Jost Tobias Springenberg*, University of Freiburg; Aaron Klein, University of Freiburg; Stefan Falkner, University of Freiburg; Frank Hutter, University of Freiburg
- Learnable Visual Markers
Oleg Grinchuk, Skolkovo Institute of Science and Technology; Vadim Lebedev, Skolkovo Institute of Science and Technology; Victor Lempitsky*,
- Fast Algorithms for Robust PCA via Gradient Descent
Xinyang Yi*, UT Austin; Dohyung Park, University of Texas at Austin; Yudong Chen, ; Constantine Caramanis,
- One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
Michalis K. Titsias*,
- Learning Deep Embeddings with Histogram Loss
Evgeniya Ustinova, Skoltech; Victor Lempitsky*,
- Spectral Learning of Dynamic Systems from Nonequilibrium Data
Hao Wu*, Free University of Berlin; Frank Noe,
- Markov Chain Sampling in Discrete Probabilistic Models with Constraints
Chengtao Li*, MIT; Suvrit Sra, MIT; Stefanie Jegelka, MIT
- Mapping Estimation for Discrete Optimal Transport
Michael Perrot*, University of Saint-Etienne, laboratoire Hubert Curien; Nicolas Courty, ; Rémi Flamary, ; Amaury Habrard, University of Saint-Etienne, Laboratoire Hubert Curien
- BBO-DPPs: Batched Bayesian Optimization via Determinantal Point Processes
Tarun Kathuria*, Microsoft Research; Amit Deshpande, ; Pushmeet Kohli,
- Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images
Vladimir Golkov*, Technical University of Munich; Marcin Skwark, Vanderbilt University; Antonij Golkov, University of Augsburg; Alexey Dosovitskiy, ; Thomas Brox, University of Freiburg; Jens Meiler, Vanderbilt University; Daniel Cremers, Technical University of Munich
- Linear Feature Encoding for Reinforcement Learning
Zhao Song*, Duke University; Ronald Parr, ; Xuejun Liao, Duke University; Lawrence Carin,
- A Minimax Approach to Supervised Learning
Farzan Farnia*, Stanford University; David Tse, Stanford University
- Edge-Exchangeable Graphs and Sparsity
Diana Cai*, University of Chicago; Trevor Campbell, MIT; Tamara Broderick, MIT
- A Locally Adaptive Normal Distribution
Georgios Arvanitidis*, DTU; Lars Kai Hansen, ; Søren Hauberg,
- Completely random measures for modelling block-structured sparse networks
Tue Herlau*, ; Mikkel Schmidt, DTU; Morten Mørup, Technical University of Denmark
- Sparse Support Recovery with Non-smooth Loss Functions
Kévin Degraux*, Université catholique de Louva; Gabriel Peyré, ; Jalal Fadili, ; Laurent Jacques, Université catholique de Louvain
- Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
Travis Monk*, University of Oldenburg; Cristina Savin, IST Austria; Jörg Lücke,
- Learning values across many orders of magnitude
Hado Van Hasselt*, ; Arthur Guez, ; Matteo Hessel, Google DeepMind; Volodymyr Mnih, ; David Silver,
- Adaptive Smoothed Online Multi-Task Learning
Keerthiram Murugesan*, Carnegie Mellon University; Hanxiao Liu, Carnegie Mellon University; Jaime Carbonell, CMU; Yiming Yang, CMU
- Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, ETH Zurich; Felix Berkenkamp*, ETH Zurich; Andreas Krause,
- Probabilistic Linear Multistep Methods
Onur Teymur*, Imperial College London; Kostas Zygalakis, ; Ben Calderhead,
- Stochastic Three-Composite Convex Minimization
Alp Yurtsever*, EPFL; Bang Vu, ; Volkan Cevher,
- Using Fast Weights to Attend to the Recent Past
Jimmy Ba*, University of Toronto; Geoffrey Hinton, Google; Volodymyr Mnih, ; Joel Leibo, Google DeepMind; Catalin Ionescu, Google
- Maximal Sparsity with Deep Networks?
Bo Xin*, Peking University; Yizhou Wang, Peking University; Wen Gao, peking university; David Wipf,
- Quantifying and Reducing Stereotypes in Word Embeddings
Tolga Bolukbasi*, Boston University; Kai-Wei Chang, ; James Zou, ; Venkatesh Saligrama, ; Adam Kalai, Microsoft Research
- beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
Valentina Zantedeschi*, UJM Saint-Etienne, France; Rémi Emonet, ; Marc Sebban,
- Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
Xiaotong Yuan*, Nanjing University of Informat; Ping Li, ; Tong Zhang, ; Qingshan Liu, ; Guangcan Liu, NUIST
- Backprop KF: Learning Discriminative Deterministic State Estimators
Tuomas Haarnoja*, UC Berkeley; Anurag Ajay, UC Berkeley; Sergey Levine, University of Washington; Pieter Abbeel,
- 2-Component Recurrent Neural Networks
Xiang Li*, NJUST; Tao Qin, Microsoft; Jian Yang, ; Xiaolin Hu, ; Tie-Yan Liu, Microsoft Research
- Fast recovery from a union of subspaces
Chinmay Hegde, ; Piotr Indyk, MIT; Ludwig Schmidt*, MIT
- Incremental Learning for Variational Sparse Gaussian Process Regression
Ching-An Cheng*, Georgia Institute of Technolog; Byron Boots,
- A Consistent Regularization Approach for Structured Prediction
Carlo Ciliberto*, MIT; Lorenzo Rosasco, ; Alessandro Rudi,
- Clustering Signed Networks with the Geometric Mean of Laplacians
Pedro Eduardo Mercado Lopez*, Saarland University; Francesco Tudisco, Saarland University; Matthias Hein, Saarland University
- An urn model for majority voting in classification ensembles
Víctor Soto, Columbia University; Alberto Suarez, ; Gonzalo Martínez-Muñoz*,
- Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
Jacob Steinhardt*, Stanford University; Gregory Valiant, ; Moses Charikar, Stanford University
- Fast and accurate spike sorting of high-channel count probes with KiloSort
Marius Pachitariu*, ; Nick Steinmetz, UCL; Shabnam Kadir, ; Matteo Carandini, UCL; Kenneth Harris, UCL
- Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
Wouter M. Koolen*, ; Peter Grunwald, CWI; Tim Van Erven,
- Ancestral Causal Inference
Sara Magliacane*, VU University Amsterdam; Tom Claassen, ; Joris Mooij, Radboud University Nijmegen
- More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Xinyang Yi, UT Austin; Zhaoran Wang, Princeton University; Zhuoran Yang , Princeton University; Constantine Caramanis, ; Han Liu*,
- Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff*, IDSIA; Antti Rasmus, The Curious AI Company; Mathias Berglund, The Curious AI Company; Tele Hao, The Curious AI Company; Harri Valpola, The Curious AI Company
- Efficient Algorithm for Streaming Submodular Cover
Ashkan Norouzi-Fard*, EPFL; Abbas Bazzi, EPFL; Ilija Bogunovic, EPFL Lausanne; Marwa El Halabi, l; Ya-Ping Hsieh, ; Volkan Cevher,
- Interaction Networks for Learning about Objects, Relations and Physics
Peter Battaglia*, Google DeepMind; Razvan Pascanu, ; Matthew Lai, Google DeepMind; Danilo Jimenez Rezende, ; Koray Kavukcuoglu, Google DeepMind
- Efficient state-space modularization for planning: theory, behavioral and neural signatures
Daniel McNamee*, University of Cambridge; Daniel Wolpert, University of Cambridge; Máté Lengyel, University of Cambridge
- Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Chi Jin*, UC Berkeley; Sham Kakade, ; Praneeth Netrapalli, Microsoft Research
- Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
Wei-Shou Hsu*, University of Waterloo; Pascal Poupart,
- Computing and maximizing influence in linear threshold and triggering models
Justin Khim*, University of Pennsylvania; Varun Jog, ; Po-Ling Loh, Berkeley
- Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Yichen Wang*, Georgia Tech; Nan Du, ; Rakshit Trivedi, Georgia Institute of Technolo; Le Song,
- Learning Deep Parsimonious Representations
Renjie Liao*, UofT; Alexander Schwing, ; Rich Zemel, ; Raquel Urtasun,
- Optimal Learning for Multi-pass Stochastic Gradient Methods
Junhong Lin*, Istituto Italiano di Tecnologia; Lorenzo Rosasco,
- Generative Adversarial Imitation Learning
Jonathan Ho*, Stanford; Stefano Ermon,
- An End-to-End Approach for Natural Language to IFTTT Program Translation
Chang Liu*, University of Maryland; Xinyun Chen, Shanghai Jiaotong University; Richard Shin, ; Mingcheng Chen, University of Illinois, Urbana-Champaign; Dawn Song, UC Berkeley
- Dual Space Gradient Descent for Online Learning
Trung Le*, University of Pedagogy Ho Chi Minh city; Tu Nguyen, Deakin University; Vu Nguyen, Deakin University; Dinh Phung, Deakin University
- Fast stochastic optimization on Riemannian manifolds
Hongyi Zhang*, MIT; Sashank Jakkam Reddi, Carnegie Mellon University; Suvrit Sra, MIT
- Professor Forcing: A New Algorithm for Training Recurrent Networks
Alex Lamb, Montreal; Anirudh Goyal*, University of Montreal; ying Zhang, University of Montreal; Saizheng Zhang, University of Montreal; Aaron Courville, University of Montreal; Yoshua Bengio, U. Montreal
- Learning brain regions via large-scale online structured sparse dictionary learning
Elvis DOHMATOB*, Inria; Arthur Mensch, inria; Gaël Varoquaux, ; Bertrand Thirion,
- Efficient Neural Codes under Metabolic Constraints
Zhuo Wang*, University of Pennsylvania; Xue-Xin Wei, University of Pennsylvania; Alan Stocker, ; Dan Lee , University of Pennsylvania
- Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University
- Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
Alexander Shishkin, Yandex; Anastasia Bezzubtseva, Yandex; Alexey Drutsa*, Yandex; Ilia Shishkov, Yandex; Ekaterina Gladkikh, Yandex; Gleb Gusev, Yandex LLC; Pavel Serdyukov, Yandex
- Bayesian Intermittent Demand Forecasting for Large Inventories
Matthias Seeger*, Amazon; David Salinas, Amazon; Valentin Flunkert, Amazon
- Visual Question Answering with Question Representation Update
RUIYU LI*, CUHK; Jiaya Jia, CUHK
- Learning Parametric Sparse Models for Image Super-Resolution
Yongbo Li, Xidian University; Weisheng Dong*, Xidian University; GUANGMING Shi, Xidian University; Xuemei Xie, Xidian University; Xin Li, WVU
- Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
Jean-Bastien Grill, Inria Lille - Nord Europe; Michal Valko*, Inria Lille - Nord Europe; Remi Munos, Google DeepMind
- Asynchronous Parallel Greedy Coordinate Descent
Yang You, UC Berkeley; Xiangru Lian, University of Rochester; Cho-Jui Hsieh*, ; Ji Liu, ; Hsiang-Fu Yu, University of Texas at Austin; Inderjit Dhillon, ; James Demmel, UC Berkeley
- Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Rex Devon Hjelm*, University of New Mexico; Ruslan Salakhutdinov, University of Toronto; Kyunghyun Cho, University of Montreal; Nebojsa Jojic, Microsoft Research; Vince Calhoun, Mind Research Network; Junyoung Chung, University of Montreal
- Assortment Optimization Under the Mallows model
Antoine Desir*, Columbia University; Vineet Goyal, ; Srikanth Jagabathula, ; Danny Segev,
- Disease Trajectory Maps
Peter Schulam*, Johns Hopkins University; Raman Arora,
- Multistage Campaigning in Social Networks
Mehrdad Farajtabar*, Georgia Tech; Xiaojing Ye, Georgia State University; Sahar Harati, Emory University; Le Song, ; Hongyuan Zha, Georgia Institute of Technology
- Learning in Games: Robustness of Fast Convergence
Dylan Foster, Cornell University; Zhiyuan Li, Tsinghua University; Thodoris Lykouris*, Cornell University; Karthik Sridharan, Cornell University; Eva Tardos, Cornell University
- Improving Variational Autoencoders with Inverse Autoregressive Flow
Diederik Kingma*, ; Tim Salimans,
- Algorithms and matching lower bounds for approximately-convex optimization
Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University
- Unified Methods for Exploiting Piecewise Structure in Convex Optimization
Tyler Johnson*, University of Washington; Carlos Guestrin,
- Kernel Bayesian Inference with Posterior Regularization
Yang Song*, Stanford University; Jun Zhu, ; Yong Ren, Tsinghua University
- Neural universal discrete denoiser
Taesup Moon*, DGIST; Seonwoo Min, Seoul National University; Byunghan Lee, Seoul National University; Sungroh Yoon, Seoul National University - Optimal Architectures in a Solvable Model of Deep Networks
Jonathan Kadmon*, Hebrew University; Haim Sompolinsky ,
- Conditional Image Generation with Pixel CNN Decoders
Aaron Van den Oord*, Google Deepmind; Nal Kalchbrenner, ; Lasse Espeholt, ; Koray Kavukcuoglu, Google DeepMind; Oriol Vinyals, ; Alex Graves,
- Supervised Learning with Tensor Networks
Edwin Stoudenmire*, Univ of California Irvine; David Schwab, Northwestern University
- Multi-step learning and underlying structure in statistical models
Maia Fraser*, University of Ottawa
- Blind Optimal Recovery of Signals
Dmitry Ostrovsky*, Univ. Grenoble Alpes; Zaid Harchaoui, NYU, Courant Institute; Anatoli Juditsky, ; Arkadi Nemirovski, Gerogia Institute of Technology
- An Architecture for Deep, Hierarchical Generative Models
Philip Bachman*,
- Feature selection for classification of functional data using recursive maxima hunting
José Torrecilla*, Universidad Autónoma de Madrid; Alberto Suarez,
- Achieving budget-optimality with adaptive schemes in crowdsourcing
Ashish Khetan, University of Illinois Urbana-; Sewoong Oh*,
- Near-Optimal Smoothing of Structured Conditional Probability Matrices
Moein Falahatgar, UCSD; Mesrob I. Ohannessian*, ; Alon Orlitsky,
- Supervised Word Mover's Distance
Gao Huang, ; Chuan Guo*, Cornell University; Matt Kusner, ; Yu Sun, ; Fei Sha, University of Southern California; Kilian Weinberger,
- Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
Amin Jalali*, University of Washington; Qiyang Han, University of Washington; Ioana Dumitriu, University of Washington; Maryam Fazel, University of Washington
- Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom*, University of Washington; Thomas Powers, ; John Hershey, ; Jonathan LeRoux, ; Les Atlas,
- Threshold Bandits, With and Without Censored Feedback
Jacob Abernethy, ; Kareem Amin, ; Ruihao Zhu*, Massachusetts Institute of Technology
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Wenjie Luo*, University of Toronto; Yujia Li, University of Toronto; Raquel Urtasun, ; Rich Zemel,
- Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
Lev Bogolubsky, ; Pavel Dvurechensky*, Weierstrass Institute for Appl; Alexander Gasnikov, ; Gleb Gusev, Yandex LLC; Yurii Nesterov, ; Andrey Raigorodskii, ; Aleksey Tikhonov, ; Maksim Zhukovskii,
- k^*-Nearest Neighbors: From Global to Local
Oren Anava, Technion; Kfir Levy*, Technion
- Normalized Spectral Map Synchronization
Yanyao Shen*, UT Austin; Qixing Huang, Toyota Technological Institute at Chicago; Nathan Srebro, ; Sujay Sanghavi,
- Beyond Exchangeability: The Chinese Voting Process
Moontae Lee*, Cornell University; Seok Hyun Jin, Cornell University; David Mimno, Cornell University
- A posteriori error bounds for joint matrix decomposition problems
Nicolo Colombo, Univ of Luxembourg; Nikos Vlassis*, Adobe Research
- A Bayesian method for reducing bias in neural representational similarity analysis
Ming Bo Cai*, Princeton University; Nicolas Schuck, Princeton Neuroscience Institute, Princeton University; Jonathan Pillow, ; Yael Niv,
- Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Chris Junchi Li, Princeton University; Zhaoran Wang*, Princeton University; Han Liu,
- Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang*, University of Alberta; Tor Lattimore, ; András György, ; Csaba Szepesvari, U. Alberta
- SDP Relaxation with Randomized Rounding for Energy Disaggregation
Kiarash Shaloudegi, ; András György*, ; Csaba Szepesvari, U. Alberta; Wilsun Xu, University of Alberta
- Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Yuanzhi Li, Princeton University; Yingyu Liang*, ; Andrej Risteski, Princeton University
- Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende*, ; S. M. Ali Eslami, Google DeepMind; Shakir Mohamed, Google DeepMind; Peter Battaglia, Google DeepMind; Max Jaderberg, ; Nicolas Heess,
- Poisson-Gamma dynamical systems
Aaron Schein*, UMass Amherst; Hanna Wallach, Microsoft Research; Mingyuan Zhou,
- Gaussian Processes for Survival Analysis
Tamara Fernandez, Oxford; Nicolas Rivera*, King's College London; Yee-Whye Teh,
- Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
Ian En-Hsu Yen*, University of Texas at Austin; huang Xiangru, University of Texas at Austin; Kai Zhong, University of Texas at Austin; Zhang Ruohan, University of Texas at Austin; Pradeep Ravikumar, ; Inderjit Dhillon,
- Optimal Binary Classifier Aggregation for General Losses
Akshay Balsubramani*, UC San Diego; Yoav Freund,
- Disentangling factors of variation in deep representation using adversarial training
Michael Mathieu, NYU; Junbo Zhao, NYU; Aditya Ramesh, NYU; Pablo Sprechmann*, ; Yann LeCun, NYU
- A primal-dual method for constrained consensus optimization
Necdet Aybat*, Penn State University; Erfan Yazdandoost Hamedani, Penn State University
- Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
Farshad Lahouti *, Caltech ; Babak Hassibi, Caltech