ICML

Latent Space Factorisation and Manipulation via Matrix Subspace Projection

Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin

Comments Final camera ready version for ICML 2020

Multigrid Neural Memory

Tri Huynh, Michael Maire, Matthew R. Walter

Comments ICML 2020; Project Website: http://people.cs.uchicago.edu/~trihuynh/multigrid_mem

Knowing The What But Not The Where in Bayesian Optimization

Vu Nguyen, Michael A. Osborne

Comments 16 pages

Journal ref International Conference on Machine Learning (ICML) 2020

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates

Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev

Comments Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020

An end-to-end approach for the verification problem: learning the right distance

Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm, Tiago Falk

Comments ICML 2020 final camera ready. Code is available at: https://github.com/joaomonteirof/e2e_verification

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Steven Kleinegesse, Michael U. Gutmann

Comments Accepted at the thirty-seventh International Conference on Machine Learning (ICML) 2020. Camera-ready version

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel

Comments ICML 2020

Provable Smoothness Guarantees for Black-Box Variational Inference

Justin Domke

Comments International Conference on Machine Learning (ICML) 2020

Cooperative Multi-Agent Bandits with Heavy Tails

Abhimanyu Dubey, Alex Pentland

Comments 26 pages including appendix, camera-ready for ICML 2020

Kernel Methods for Cooperative Multi-Agent Contextual Bandits

Abhimanyu Dubey, Alex Pentland

Comments 19 pages including supplement, camera-ready at ICML 2020

Robustness to Spurious Correlations via Human Annotations

Megha Srivastava, Tatsunori Hashimoto, Percy Liang

Comments ICML 2020 final version, 16 pages, 9 figures

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine

Comments 18 pages, 13 figures, accepted to the International Conference on Machine Learning (ICML) 2020

Associative Memory in Iterated Overparameterized Sigmoid Autoencoders

Yibo Jiang, Cengiz Pehlevan

Journal ref ICML 2020

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong V. Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

Comments Published at the International Conference on Machine Learning (ICML) 2020

Projective Preferential Bayesian Optimization

Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski

Comments 9 pages, 2 figures

Journal ref In Proceedings of the 37th International Conference on Machine Learning, ICML'20, pp. 4050-4058, 2020

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter

Comments To appear ICML 2020

Confidence-Aware Learning for Deep Neural Networks

Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang

Comments ICML 2020. The first two authors contributed equally

Evaluating the Performance of Reinforcement Learning Algorithms

Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas

Comments 30 pages, 9 figures, Thirty-seventh International Conference on Machine Learning (ICML 2020)

Learning to Learn Kernels with Variational Random Features

Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek

Comments ICML'2020; code is available in: https://github.com/Yingjun-Du/MetaVRF

Learning Flat Latent Manifolds with VAEs

Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt

Comments Thirty-seventh International Conference on Machine Learning (ICML) 2020

Journal ref International Conference on Machine Learning 2020

Estimation of Bounds on Potential Outcomes For Decision Making

Maggie Makar, Fredrik D. Johansson, John Guttag, David Sontag

Journal ref ICML 2020

On Learning Language-Invariant Representations for Universal Machine Translation

Han Zhao, Junjie Hu, Andrej Risteski

Comments Appeared in ICML 2020

Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization

Feihu Huang, Songcan Chen, Heng Huang

Comments Published in ICML 2019, 43 pages. arXiv admin note: text overlap with arXiv:1907.13463

Adding Seemingly Uninformative Labels Helps in Low Data Regimes

Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith

Comments ICML 2020

The Boomerang Sampler

Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts

Comments Accepted for publication in the proceedings of ICML 2020. Code available at https://github.com/jbierkens/ICML-boomerang

Understanding Self-Attention of Self-Supervised Audio Transformers

Shu-wen Yang, Andy T. Liu, Hung-yi Lee

Comments Accepted by INTERSPEECH 2020, ICML 2020 Workshop on Self-supervision in Audio and Speech

Journal ref INTERSPEECH 2020

Non-Stationary Delayed Bandits with Intermediate Observations

Claire Vernade, Andras Gyorgy, Timothy Mann

Comments 18 pages, 17 figures, ICML 2020

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

Comments ICML final version

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

Niels Bruun Ipsen, Lars Kai Hansen

Comments Accepted to ICML 2019. This version is the submitted paper

Journal ref International Conference on Machine Learning. 2019. pp. 2951-2960

Bayesian leave-one-out cross-validation for large data

Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari

Comments Accepted to ICML 2019. This version is the submitted paper

Journal ref Thirty-sixth International Conference on Machine Learning, PMLR 97:4244-4253, 2019