ICML 2018

621 papers

A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization

Robin Vogel, Aurélien Bellet, Stéphan Clémençon

Clustering Semi-Random Mixtures of Gaussians

Aravindan Vijayaraghavan, Pranjal Awasthi

Programmatically Interpretable Reinforcement Learning

Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri

DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Arash Vahdat, William Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash

Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, Aäron Oord

The Mirage of Action-Dependent Baselines in Reinforcement Learning

George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator

Stephen Tu, Benjamin Recht

Invariance of Weight Distributions in Rectified MLPs

Russell Tsuchida, Fred Roosta, Marcus Gallagher

StrassenNets: Deep Learning with a Multiplication Budget

Michael Tschannen, Aran Khanna, Animashree Anandkumar

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

Manolis Tsakiris, René Vidal

Learning Longer-term Dependencies in RNNs with Auxiliary Losses

Trieu Trinh, Andrew Dai, Thang Luong, Quoc Le

Convergent Tree Backup and Retrace with Function Approximation

Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

Adversarial Regression with Multiple Learners

Liang Tong, Sixie Yu, Scott Alfeld, vorobeychik

Importance Weighted Transfer of Samples in Reinforcement Learning

Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli

CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

Kevin Tian, Teng Zhang, James Zou

Decoupling Gradient-Like Learning Rules from Representations

Philip Thomas, Christoph Dann, Emma Brunskill

Bayesian Uncertainty Estimation for Batch Normalized Deep Networks

Mattias Teye, Hossein Azizpour, Kevin Smith

Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees

Adrien Taylor, Bryan Van Scoy, Laurent Lessard

Chi-square Generative Adversarial Network

Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke

Best Arm Identification in Linear Bandits with Linear Dimension Dependency

Chao Tao, Saúl Blanco, Yuan Zhou

Black Box FDR

Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan

Neural Inverse Rendering for General Reflectance Photometric Stereo

Tatsunori Taniai, Takanori Maehara

D^2: Decentralized Training over Decentralized Data

Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu

Learning the Reward Function for a Misspecified Model

Erik Talvitie

Differentiable Compositional Kernel Learning for Gaussian Processes

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li

Convolutional Imputation of Matrix Networks

Qingyun Sun, Mengyuan Yan, David Donoho, boyd

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang

Scalable approximate Bayesian inference for particle tracking data

Ruoxi Sun, Liam Paninski

Neural Program Synthesis from Diverse Demonstration Videos

Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, Joseph Lim

Stagewise Safe Bayesian Optimization with Gaussian Processes

Yanan Sui, Vincent Zhuang, Joel Burdick, Yisong Yue

Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search

Masanori Suganuma, Mete Ozay, Takayuki Okatani

Learning Low-Dimensional Temporal Representations

Bing Su, Ying Wu

Approximation Algorithms for Cascading Prediction Models

Matthew Streeter

Structured Control Nets for Deep Reinforcement Learning

Mario Srouji, Jian Zhang, Ruslan Salakhutdinov

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn

Knowledge Transfer with Jacobian Matching

Suraj Srinivas, Francois Fleuret

Accelerating Natural Gradient with Higher-Order Invariance

Yang Song, Jiaming Song, Stefano Ermon

An Inference-Based Policy Gradient Method for Learning Options

Matthew Smith, Herke Hoof, Joelle Pineau

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton

K-means clustering using random matrix sparsification

Kaushik Sinha

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, Taylan Cemgil, Gaël RICHARD

CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning

Wissam Siblini, Pascale Kuntz, Frank Meyer

TACO: Learning Task Decomposition via Temporal Alignment for Control

Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, Jun Zhu

An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method

Li Shen, peng sun, Yitong Wang, Wei Liu, Tong Zhang

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian

Learning in Integer Latent Variable Models with Nested Automatic Differentiation

Daniel Sheldon, Kevin Winner, Debora Sujono

Locally Private Hypothesis Testing

Or Sheffet

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Noam Shazeer, Mitchell Stern

Solving Partial Assignment Problems using Random Clique Complexes

Charu Sharma, Deepak Nathani, Manohar Kaul

Finding Influential Training Samples for Gradient Boosted Decision Trees

Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten Rijke

First Order Generative Adversarial Networks

Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter

Bounding and Counting Linear Regions of Deep Neural Networks

Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam

Overcoming Catastrophic Forgetting with Hard Attention to the Task

Joan Serrà, Didac Suris, Marius Miron, Alexandros Karatzoglou

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

Progress & Compress: A scalable framework for continual learning

Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh

Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care

Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle

Learning with Abandonment

Sven Schmit, Ramesh Johari

Tight Regret Bounds for Bayesian Optimization in One Dimension

Jonathan Scarlett

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade

A Classification-Based Study of Covariate Shift in GAN Distributions

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

Graph Networks as Learnable Physics Engines for Inference and Control

Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller

Representation Tradeoffs for Hyperbolic Embeddings

Frederic Sala, Chris De Sa, Albert Gu, Christopher Re

Tempered Adversarial Networks

Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf

Learning Equations for Extrapolation and Control

Subham Sahoo, Christoph Lampert, Georg Martius

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

Itay Safran, Ohad Shamir

Black-Box Variational Inference for Stochastic Differential Equations

Tom Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle

Probabilistic Boolean Tensor Decomposition

Tammo Rukat, Chris Holmes, Christopher Yau

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Francisco Ruiz, Michalis Titsias, Adji Bousso Dieng, David Blei

Deep One-Class Classification

Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui

Fast Information-theoretic Bayesian Optimisation

Binxin Ru, Michael A. Osborne, Mark McLeod, Diego Granziol

Learning to Optimize Combinatorial Functions

Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck

Been There, Done That: Meta-Learning with Episodic Recall

Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell

Learning by Playing Solving Sparse Reward Tasks from Scratch

Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun

Weightless: Lossy weight encoding for deep neural network compression

Brandon Reagan, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander Rush

Learning Implicit Generative Models with the Method of Learned Moments

Suman Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals

Gradient Coding from Cyclic MDS Codes and Expander Graphs

Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate

Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan

Tighter Variational Bounds are Not Necessarily Better

Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl

On Nesting Monte Carlo Estimators

Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, Frank Wood

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

Roberta Raileanu, Emily Denton, arthur szlam, Rob Fergus

Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation

Hugo Raguet, Loic Landrieu

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

Maithra Raghu, Alex Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le

Fast Parametric Learning with Activation Memorization

Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap

Machine Theory of Mind

Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami

Non-convex Conditional Gradient Sliding

Chao Qu, Yan Li, Huan Xu

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

Qiang Qiu, Xiuyuan Cheng, Calderbank, Guillermo Sapiro

Gradually Updated Neural Networks for Large-Scale Image Recognition

Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo Wang, Alan Yuille

Do Outliers Ruin Collaboration?

Mingda Qiao

Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction

Siyuan Qi, Baoxiong Jia, Song-chun Zhu

Selecting Representative Examples for Program Synthesis

Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Kaelbling

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang

Learning Dynamics of Linear Denoising Autoencoders

Arnu Pretorius, Steve Kroon, Herman Kamper

Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory

Guillaume Pouliot

Local Convergence Properties of SAGA/Prox-SVRG and Acceleration

Clarice Poon, Jingwei Liang, Carola Schoenlieb

Constant-Time Predictive Distributions for Gaussian Processes

Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson

Bandits with Delayed, Aggregated Anonymous Feedback

Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvári, Steffen Grünewälder