ICML 2017

434 papers

Stochastic Gradient MCMC Methods for Hidden Markov Models

Yi-An Ma, Nicholas J. Foti, Emily B. Fox

Spherical Structured Feature Maps for Kernel Approximation

Yueming LYU

Learning Gradient Descent: Better Generalization and Longer Horizons

Kaifeng Lv, Shunhua Jiang, Jian Li

Learning Deep Architectures via Generalized Whitened Neural Networks

Ping Luo

How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?

Andreas Loukas

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks

Christos Louizos, Max Welling

Deep Transfer Learning with Joint Adaptation Networks

Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan

Learning Infinite Layer Networks Without the Kernel Trick

Roi Livni, Daniel Carmon, Amir Globerson

Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh

Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization

Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Dimitris N. Metaxas

Analogical Inference for Multi-relational Embeddings

Hanxiao Liu, Yuexin Wu, Yiming Yang

Algorithmic Stability and Hypothesis Complexity

Tongliang Liu, Gabor Lugosi, Gergely Neu, Dacheng Tao

Iterative Machine Teaching

Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu

Zero-Inflated Exponential Family Embeddings

Li-Ping Liu, David M. Blei

Leveraging Union of Subspace Structure to Improve Constrained Clustering

John Lipor, Laura Balzano

Exact MAP Inference by Avoiding Fractional Vertices

Erik M. Lindgren, Alexandros G. Dimakis, Adam Klivans

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization

Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney

Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms

Qianxiao Li, Cheng Tai, Weinan E

Forest-type Regression with General Losses and Robust Forest

Alexander Hanbo Li, Andrew Martin

Fast k-Nearest Neighbour Search via Prioritized DCI

Ke Li, Jitendra Malik

Provably Optimal Algorithms for Generalized Linear Contextual Bandits

Lihong Li, Yu Lu, Dengyong Zhou

Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations

Yuanzhi Li, Yingyu Liang

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Yingzhen Li, Yarin Gal

Learning to Align the Source Code to the Compiled Object Code

Dor Levy, Lior Wolf

Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization

Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar

Deriving Neural Architectures from Sequence and Graph Kernels

Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola

Confident Multiple Choice Learning

Kimin Lee, Changho Hwang, Kyoungsoo Park, Jinwoo Shin

Bayesian inference on random simple graphs with power law degree distributions

Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi

Coordinated Multi-Agent Imitation Learning

Hoang M. Le, Yisong Yue, Peter Carr, Patrick Lucey

Deep Spectral Clustering Learning

Marc T. Law, Raquel Urtasun, Richard S. Zemel

Consistent k-Clustering

Silvio Lattanzi, Sergei Vassilvitskii

Conditional Accelerated Lazy Stochastic Gradient Descent

Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink

Co-clustering through Optimal Transport

Charlotte Laclau, Ievgen Redko, Basarab Matei, Younès Bennani, Vincent Brault

Grammar Variational Autoencoder

Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato

Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things

Ashish Kumar, Saurabh Goyal, Manik Varma

Evaluating Bayesian Models with Posterior Dispersion Indices

Alp Kucukelbir, Yixin Wang, David M. Blei

Active Learning for Cost-Sensitive Classification

Akshay Krishnamurthy, Alekh Agarwal, Tzu-kuo Huang, Hal Daume III, John Langford

PixelCNN Models with Auxiliary Variables for Natural Image Modeling

Alexander Kolesnikov, Christoph H. Lampert

Sub-sampled Cubic Regularization for Non-convex Optimization

Jonas Moritz Kohler, Aurelien Lucchi

Understanding Black-box Predictions via Influence Functions

Pang Wei Koh, Percy Liang

Cost-Optimal Learning of Causal Graphs

Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath

SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization

Juyong Kim, Yookoon Park, Gunhee Kim, Sung Ju Hwang

Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim

Graph-based Isometry Invariant Representation Learning

Renata Khasanova, Pascal Frossard

On Approximation Guarantees for Greedy Low Rank Optimization

Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis, Joydeep Ghosh, Sahand Negahban

Meritocratic Fairness for Cross-Population Selection

Michael Kearns, Aaron Roth, Zhiwei Steven Wu

Learning in POMDPs with Monte Carlo Tree Search

Sammie Katt, Frans A. Oliehoek, Christopher Amato

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics

Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

Recursive Partitioning for Personalization using Observational Data

Nathan Kallus

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP

Satyen Kale, Zohar Karnin, Tengyuan Liang, Dávid Pál

Video Pixel Networks

Nal Kalchbrenner, Aäron Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals

Differentially Private Chi-squared Test by Unit Circle Mechanism

Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma

StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent

Tyler B. Johnson, Carlos Guestrin

An Adaptive Test of Independence with Analytic Kernel Embeddings

Wittawat Jitkrittum, Zoltan Szabo, Arthur Gretton

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott Skirlo

How to Escape Saddle Points Efficiently

Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan

Efficient Nonmyopic Active Search

Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley

Contextual Decision Processes with low Bellman rank are PAC-Learnable

Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Uniform Convergence Rates for Kernel Density Estimation

Heinrich Jiang

Density Level Set Estimation on Manifolds with DBSCAN

Heinrich Jiang

From Patches to Images: A Nonparametric Generative Model

Geng Ji, Michael C. Hughes, Erik B. Sudderth

Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation

Yacine Jernite, Anna Choromanska, David Sontag

Bayesian Optimization with Tree-structured Dependencies

Rodolphe Jenatton, Cédric Archambeau, Javier Gonzalez, Matthias Seeger

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner

Scalable Generative Models for Multi-label Learning with Missing Labels

Vikas Jain, Nirbhay Modhe, Piyush Rai

Decoupled Neural Interfaces using Synthetic Gradients

Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves

Fairness in Reinforcement Learning

Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

Variational Inference for Sparse and Undirected Models

John Ingraham, Debora Marks

Tensor Decomposition with Smoothness

Masaaki Imaizumi, Kohei Hayashi

Toward Controlled Generation of Text

Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing

Deep Generative Models for Relational Data with Side Information

Changwei Hu, Piyush Rai, Lawrence Carin

State-Frequency Memory Recurrent Neural Networks

Hao Hu, Guo-Jun Qi

Learning Discrete Representations via Information Maximizing Self-Augmented Training

Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama

Dissipativity Theory for Nesterov’s Accelerated Method

Bin Hu, Laurent Lessard

Analysis and Optimization of Graph Decompositions by Lifted Multicuts

Andrea Hornakova, Jan-Hendrik Lange, Bjoern Andres

Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks

Mingyi Hong, Davood Hajinezhad, Ming-Min Zhao

Minimizing Trust Leaks for Robust Sybil Detection

János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz

Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo

Matthew D. Hoffman

Multilevel Clustering via Wasserstein Means

nhật Hồ, Xuanlong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh

SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling

Jun-ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space

José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alan Aspuru-Guzik

Warped Convolutions: Efficient Invariance to Spatial Transformations

João F. Henriques, Andrea Vedaldi

The Sample Complexity of Online One-Class Collaborative Filtering

Reinhard Heckel, Kannan Ramchandran

Kernelized Support Tensor Machines

Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen

Efficient Regret Minimization in Non-Convex Games

Elad Hazan, Karan Singh, Cyril Zhang

Robust Guarantees of Stochastic Greedy Algorithms

Avinatan Hassidim, Yaron Singer

Deep IV: A Flexible Approach for Counterfactual Prediction

Jason Hartford, Greg Lewis, Kevin Leyton-brown, Matt Taddy

Joint Dimensionality Reduction and Metric Learning: A Geometric Take

Mehrtash Harandi, Mathieu Salzmann, Richard Hartley

Data-Efficient Policy Evaluation Through Behavior Policy Search

Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum

Faster Greedy MAP Inference for Determinantal Point Processes

Insu Han, Prabhanjan Kambadur, Kyoungsoo Park, Jinwoo Shin

Consistent On-Line Off-Policy Evaluation

Assaf Hallak, Shie Mannor

DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres, François Pachet, Frank Nielsen

Reinforcement Learning with Deep Energy-Based Policies

Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

Michael Gygli, Mohammad Norouzi, Anelia Angelova

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices

Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape

On Calibration of Modern Neural Networks

Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger

Automated Curriculum Learning for Neural Networks

Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu

Efficient softmax approximation for GPUs

Grave, Armand Joulin, Moustapha Cisse, David Grangier, Herve Jegou