ICML

Provable Guarantees for Gradient-Based Meta-Learning

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar

Comments ICML 2019

Adversarial Generation of Time-Frequency Features with application in audio synthesis

Andrés Marafioti, Nicki Holighaus, Nathanaël Perraudin, Piotr Majdak

Comments Accepted for publication at ICML 2019

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel

Comments Accepted at ICML 2019

Neural separation of observed and unobserved distributions

Tavi Halperin, Ariel Ephrat, Yedid Hoshen

Comments ICML'19

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang

Comments icml 2019 camera-ready version

MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means

Matthieu Lerasle, Zoltan Szabo, Timothee Mathieu, Guillaume Lecue

Comments ICML-2019: camera-ready paper. Code: https://bitbucket.org/TimotheeMathieu/monk-mmd

Output-Constrained Bayesian Neural Networks

Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez

Comments Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning and Workshop on Understanding and Improving Generalization in Deep Learning. Long Beach, CA, 2019

Spectral Clustering of Signed Graphs via Matrix Power Means

Pedro Mercado, Francesco Tudisco, Matthias Hein

Comments final version accepted at ICML 2019

Distributional Reinforcement Learning for Efficient Exploration

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

Journal ref ICML, 2019

Distribution Calibration for Regression

Hao Song, Tom Diethe, Meelis Kull, Peter Flach

Comments ICML 2019, 10 pages

Geometric Losses for Distributional Learning

Arthur Mensch, Mathieu Blondel, Gabriel Peyré

Journal ref Proceedings of the International Conference on Machine Learning, 2019, Long Beach, United States

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang

Comments ICML 2019

Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function

Arvind U. Raghunathan, Anoop Cherian, Devesh K. Jha

Comments Accepted at International Conference on Machine Learning (ICML), 2019

Learning What and Where to Transfer

Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin

Comments Accepted to ICML 2019

Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Angel Bautista, Shih-Yu Sun, Carlos Guestrin, Josh Susskind

Comments Accepted to ICML 2019

Approximating Orthogonal Matrices with Effective Givens Factorization

Thomas Frerix, Joan Bruna

Comments International Conference on Machine Learning (ICML 2019)

Learning Generative Models across Incomparable Spaces

Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka

Comments International Conference on Machine Learning (ICML)

Journal ref Proceedings of Machine Learning Research (PMLR), 97 (2019)

Relational Pooling for Graph Representations

Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro

Comments ICML 2019 Camera-Ready. Added to molecular experiments and balanced the classes of the validation folds for the synthetic-graph experiments. Clarified some discussions

Hyperbolic Disk Embeddings for Directed Acyclic Graphs

Ryota Suzuki, Ryusuke Takahama, Shun Onoda

Comments Accepted for ICML 2019; Camera ready version

BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

Asa Cooper Stickland, Iain Murray

Comments Accepted for publication at ICML 2019

Learning Action Representations for Reinforcement Learning

Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip S. Thomas

Comments In Proceedings of the 36th International Conference on Machine Learning (ICML 2019)

Fairwashing: the risk of rationalization

Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp

Comments Accepted to ICML 2019

Cross-Domain 3D Equivariant Image Embeddings

Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia

Comments Accepted to the International Conference on Machine Learning, ICML 2019

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging

Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar

Comments To appear in ICML 2019. More rounds of experiments are performed before being taken average of compared to versions before

Graphite: Iterative Generative Modeling of Graphs

Aditya Grover, Aaron Zweig, Stefano Ermon

Comments ICML 2019

Graph Convolutional Gaussian Processes

Ian Walker, Ben Glocker

Comments Accepted at ICML 2019

Trajectory-Based Off-Policy Deep Reinforcement Learning

Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel

Comments Includes appendix. Accepted for ICML 2019

Imputing Missing Events in Continuous-Time Event Streams

Hongyuan Mei, Guanghui Qin, Jason Eisner

Comments ICML 2019 camera-ready. The first version of this work appeared on OpenReview in September 2018

Moment-Based Variational Inference for Markov Jump Processes

Christian Wildner, Heinz Koeppl

Comments Accepted by the 36th International Conference on Machine Learning (ICML 2019)

Deep Gaussian Processes with Importance-Weighted Variational Inference

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth

Comments Appearing ICML 2019