ICML 2016

322 papers

Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods

Huikang Liu, Weijie Wu, Anthony Man-Cho So

A Simple and Provable Algorithm for Sparse Diagonal CCA

Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Yingfei Wang, Chu Wang, Warren Powell

Markov Latent Feature Models

Aonan Zhang, John Paisley

Unitary Evolution Recurrent Neural Networks

Martin Arjovsky, Amar Shah, Yoshua Bengio

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen-Zhu, Zheng Qu, Peter Richtarik, Yang Yuan

Deep Structured Energy Based Models for Anomaly Detection

Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang

Sparse Parameter Recovery from Aggregated Data

Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Zeyuan Allen-Zhu, Yang Yuan

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er

Generative Adversarial Text to Image Synthesis

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Yarin Gal, Zoubin Ghahramani

Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak

Estimating Maximum Expected Value through Gaussian Approximation

Carlo D’Eramo, Marcello Restelli, Alessandro Nuara

Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow

Huishuai Zhang, Yuejie Chi, Yingbin Liang

Low-rank tensor completion: a Riemannian manifold preconditioning approach

Hiroyuki Kasai, Bamdev Mishra

Compressive Spectral Clustering

Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Yuchen Zhang, Jason D. Lee, Michael I. Jordan

Structured Prediction Energy Networks

David Belanger, Andrew McCallum

Anytime Exploration for Multi-armed Bandits using Confidence Information

Kwang-Sung Jun, Robert Nowak

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Nadav Cohen, Amnon Shashua

Boolean Matrix Factorization and Noisy Completion via Message Passing

Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner

Fast k-means with accurate bounds

James Newling, Francois Fleuret

Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation

David Wipf

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Yossi Arjevani, Ohad Shamir

BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces

Shane Carr, Roman Garnett, Cynthia Lo

A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation

Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed Elgammal

PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem

Yahel David, Nahum Shimkin

Correlation Clustering and Biclustering with Locally Bounded Errors

Gregory Puleo, Olgica Milenkovic

A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms

Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda

Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends

Christopher Tosh

Estimating Structured Vector Autoregressive Models

Igor Melnyk, Arindam Banerjee

PAC learning of Probabilistic Automaton based on the Method of Moments

Hadrien Glaude, Olivier Pietquin

Online Learning with Feedback Graphs Without the Graphs

Alon Cohen, Tamir Hazan, Tomer Koren

The Information-Theoretic Requirements of Subspace Clustering with Missing Data

Daniel Pimentel-Alarcon, Robert Nowak

Minimizing the Maximal Loss: How and Why

Shai Shalev-shwartz, Yonatan Wexler

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi

On the Quality of the Initial Basin in Overspecified Neural Networks

Itay Safran, Ohad Shamir

A Neural Autoregressive Approach to Collaborative Filtering

Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou

Heteroscedastic Sequences: Beyond Gaussianity

Oren Anava, Shie Mannor

SDCA without Duality, Regularization, and Individual Convexity

Shai Shalev-shwartz

Hyperparameter optimization with approximate gradient

Fabian Pedregosa

Doubly Decomposing Nonparametric Tensor Regression

Masaaki Imaizumi, Kohei Hayashi

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose

Loss factorization, weakly supervised learning and label noise robustness

Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni

Variance Reduction for Faster Non-Convex Optimization

Zeyuan Allen-Zhu, Elad Hazan

Community Recovery in Graphs with Locality

Yuxin Chen, Govinda Kamath, Changho Suh, David Tse

Smooth Imitation Learning for Online Sequence Prediction

Hoang Le, Andrew Kang, Yisong Yue, Peter Carr

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Ke Li, Jitendra Malik

Fast Rate Analysis of Some Stochastic Optimization Algorithms

Chao Qu, Huan Xu, Chong Ong

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li

Stochastic Quasi-Newton Langevin Monte Carlo

Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël RICHARD

A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization

Frank Curtis

Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit

Jie Shen, Ping Li, Huan Xu

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

Yuting Zhang, Kibok Lee, Honglak Lee

Exact Exponent in Optimal Rates for Crowdsourcing

Chao Gao, Yu Lu, Dengyong Zhou

Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs

Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien

Experimental Design on a Budget for Sparse Linear Models and Applications

Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh

A Kronecker-factored approximate Fisher matrix for convolution layers

Roger Grosse, James Martens

Network Morphism

Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen

Learning privately from multiparty data

Jihun Hamm, Yingjun Cao, Mikhail Belkin

Stability of Controllers for Gaussian Process Forward Models

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-tuong, Anne Romer, Henner Schmidt

Optimality of Belief Propagation for Crowdsourced Classification

Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi

Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

Rie Johnson, Tong Zhang

A Random Matrix Approach to Echo-State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi

Large-Margin Softmax Loss for Convolutional Neural Networks

Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

Parameter Estimation for Generalized Thurstone Choice Models

Milan Vojnovic, Seyoung Yun

Efficient Private Empirical Risk Minimization for High-dimensional Learning

Shiva Prasad Kasiviswanathan, Hongxia Jin

Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie, Ross Girshick, Ali Farhadi

Fast methods for estimating the Numerical rank of large matrices

Shashanka Ubaru, Yousef Saad

Beyond CCA: Moment Matching for Multi-View Models

Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien

Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient

Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi

Structure Learning of Partitioned Markov Networks

Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu

Learning Physical Intuition of Block Towers by Example

Adam Lerer, Sam Gross, Rob Fergus

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Adish Singla, Sebastian Tschiatschek, Andreas Krause

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

Rodolphe Jenatton, Jim Huang, Cédric Archambeau

Online Stochastic Linear Optimization under One-bit Feedback

Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-Hua Zhou

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

Learning from Multiway Data: Simple and Efficient Tensor Regression

Rose Yu, Yan Liu

Adaptive Sampling for SGD by Exploiting Side Information

Siddharth Gopal

A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Matthew Hoffman, David Blei

Binary embeddings with structured hashed projections

Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar

Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams

Roy Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar

Hierarchical Variational Models

Rajesh Ranganath, Dustin Tran, David Blei

Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alex Smola

Linking losses for density ratio and class-probability estimation

Aditya Menon, Cheng Soon Ong

Low-Rank Matrix Approximation with Stability

Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang

Variable Elimination in the Fourier Domain

Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman

A Kernelized Stein Discrepancy for Goodness-of-fit Tests

Qiang Liu, Jason Lee, Michael Jordan

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer

Convergence of Stochastic Gradient Descent for PCA

Ohad Shamir

Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity

Ohad Shamir

Accurate Robust and Efficient Error Estimation for Decision Trees

Lixin Fan

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

Giwoong Lee, Eunho Yang, Sung Hwang

Multi-Bias Non-linear Activation in Deep Neural Networks

Hongyang Li, Wanli Ouyang, Xiaogang Wang

The Variational Nystrom method for large-scale spectral problems

Max Vladymyrov, Miguel Carreira-Perpinan