ICML 2018

621 papers

Detecting and Correcting for Label Shift with Black Box Predictors

Zachary Lipton, Yu-Xiang Wang, Alexander Smola

Level-Set Methods for Finite-Sum Constrained Convex Optimization

Qihang Lin, Runchao Ma, Tianbao Yang

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Junhong Lin, Volkan Cevher

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Junhong Lin, Volkan Cevher

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon

The Dynamics of Learning: A Random Matrix Approach

Zhenyu Liao, Romain Couillet

On the Spectrum of Random Features Maps of High Dimensional Data

Zhenyu Liao, Romain Couillet

RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox

Asynchronous Decentralized Parallel Stochastic Gradient Descent

Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu

Estimation of Markov Chain via Rank-Constrained Likelihood

Xudong Li, Mengdi Wang, Anru Zhang

The Well-Tempered Lasso

Yuanzhi Li, Yoram Singer

Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering

Pan Li, Olgica Milenkovic

On the Limitations of First-Order Approximation in GAN Dynamics

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

Towards Binary-Valued Gates for Robust LSTM Training

Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin

An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks

Qianxiao Li, Shuji Hao

Out-of-sample extension of graph adjacency spectral embedding

Keith Levin, Fred Roosta, Michael Mahoney, Carey Priebe

Noise2Noise: Learning Image Restoration without Clean Data

Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras

Deep Asymmetric Multi-task Feature Learning

Hae Beom Lee, Eunho Yang, Sung Ju Hwang

Gated Path Planning Networks

Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov

Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling

Kyowoon Lee, Sol-A Kim, Jaesik Choi, Seong-Whan Lee

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

Yoonho Lee, Seungjin Choi

Hierarchical Imitation and Reinforcement Learning

Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue

The Multilinear Structure of ReLU Networks

Thomas Laurent, James Brecht

Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global

Thomas Laurent, James Brecht

Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering

Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres

An Estimation and Analysis Framework for the Rasch Model

Andrew Lan, Mung Chiang, Christoph Studer

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

Brenden Lake, Marco Baroni

Canonical Tensor Decomposition for Knowledge Base Completion

Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski

Binary Partitions with Approximate Minimum Impurity

Eduardo Laber, Marco Molinaro, Felipe Mello Pereira

Mixed batches and symmetric discriminators for GAN training

Thomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek

Understanding the Loss Surface of Neural Networks for Binary Classification

SHIYU LIANG, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant

Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Xuhong Li, Yves Grandvalet, Franck Davoine

Data-Dependent Stability of Stochastic Gradient Descent

Ilja Kuzborskij, Christoph Lampert

Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings

Aviral Kumar, Sunita Sarawagi, Ujjwal Jain

Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice

Alan Kuhnle, J. David Smith, Victoria Crawford, My Thai

Semiparametric Contextual Bandits

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

Dynamic Evaluation of Neural Sequence Models

Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals

Compiling Combinatorial Prediction Games

Frédéric Koriche

On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Risi Kondor, Shubhendu Trivedi

Nonconvex Optimization for Regression with Fairness Constraints

Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao

Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework

Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamas Keviczky

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection

Jeremias Knoblauch, Theodoros Damoulas

Crowdsourcing with Arbitrary Adversaries

Matthaeus Kleindessner, Pranjal Awasthi

An Alternative View: When Does SGD Escape Local Minima?

Bobby Kleinberg, Yuanzhi Li, Yang Yuan

Neural Relational Inference for Interacting Systems

Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

Semi-Amortized Variational Autoencoders

Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, Alexander Rush

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler

Self-Bounded Prediction Suffix Tree via Approximate String Matching

Dongwoo Kim, Christian Walder

Disentangling by Factorising

Hyunjik Kim, Andriy Mnih

Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data

Minyoung Kim

Blind Justice: Fairness with Encrypted Sensitive Attributes

Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi

Geometry Score: A Method For Comparing Generative Adversarial Networks

Valentin Khrulkov, Ivan Oseledets

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Mohammad Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal

Convergence guarantees for a class of non-convex and non-smooth optimization problems

Koulik Khamaru, Martin Wainwright

Frank-Wolfe with Subsampling Oracle

Thomas Kerdreux, Fabian Pedregosa, Alexandre d’Aspremont

ContextNet: Deep learning for Star Galaxy Classification

Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez

Improved nearest neighbor search using auxiliary information and priority functions

Omid Keivani, Kaushik Sinha

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke, Konrad Zołna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler

Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints

Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Feasible Arm Identification

Julian Katz-Samuels, Clay Scott

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Angelos Katharopoulos, Francois Fleuret

Riemannian Stochastic Recursive Gradient Algorithm

Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra

LaVAN: Localized and Visible Adversarial Noise

Danny Karmon, Daniel Zoran, Yoav Goldberg

Continual Reinforcement Learning with Complex Synapses

Christos Kaplanis, Murray Shanahan, Claudia Clopath

Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games

Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher

Improving Sign Random Projections With Additional Information

Keegan Kang, Weipin Wong

Policy Optimization with Demonstrations

Bingyi Kang, Zequn Jie, Jiashi Feng

Semi-Supervised Learning via Compact Latent Space Clustering

Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno

Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations

Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra

Residual Unfairness in Fair Machine Learning from Prejudiced Data

Nathan Kallus, Angela Zhou

Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit

Sreejith Kallummil, Sheetal Kalyani

Learning Diffusion using Hyperparameters

Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg

Efficient Neural Audio Synthesis

Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu

Fast Decoding in Sequence Models Using Discrete Latent Variables

LUKASZ KAISER, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar

Kronecker Recurrent Units

Cijo Jose, Moustapha Cisse, Francois Fleuret

Composite Functional Gradient Learning of Generative Adversarial Models

Rie Johnson, Tong Zhang

Large-Scale Cox Process Inference using Variational Fourier Features

ST John, James Hensman

WSNet: Compact and Efficient Networks Through Weight Sampling

Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic

Regret Minimization for Partially Observable Deep Reinforcement Learning

Peter Jin, Kurt Keutzer, Sergey Levine

Network Global Testing by Counting Graphlets

Jiashun Jin, Zheng Ke, Shengming Luo

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin, Regina Barzilay, Tommi Jaakkola

The Weighted Kendall and High-order Kernels for Permutations

Yunlong Jiao, Jean-philippe Vert

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-jia Li, Li Fei-fei

Quickshift++: Provably Good Initializations for Sample-Based Mean Shift

Heinrich Jiang, Jennifer Jang, Samory Kpotufe

Feedback-Based Tree Search for Reinforcement Learning

Daniel Jiang, Emmanuel Ekwedike, Han Liu

Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks

Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken

Efficient end-to-end learning for quantizable representations

Yeonwoo Jeong, Hyun Oh Song

A Unified Framework for Structured Low-rank Matrix Learning

Pratik Jawanpuria, Bamdev Mishra

Detecting non-causal artifacts in multivariate linear regression models

Dominik Janzing, Bernhard Schölkopf

Pathwise Derivatives Beyond the Reparameterization Trick

Martin Jankowiak, Fritz Obermeyer

Video Prediction with Appearance and Motion Conditions

Yunseok Jang, Gunhee Kim, Yale Song

Differentially Private Matrix Completion Revisited

Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta

Firing Bandits: Optimizing Crowdfunding

Lalit Jain, Kevin Jamieson

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach

Ariel Jaffe, Roi Weiss, Boaz Nadler, Shai Carmi, Yuval Kluger

Anonymous Walk Embeddings

Sergey Ivanov, Evgeny Burnaev

Unbiased Objective Estimation in Predictive Optimization

Shinji Ito, Akihiro Yabe, Ryohei Fujimaki

Deep Density Destructors

David Inouye, Pradeep Ravikumar