ICML

European Union regulations on algorithmic decision-making and a "right to explanation"

Bryce Goodman, Seth Flaxman

Comments presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY

Journal ref AI Magazine, Vol 38, No 3, 2017

On Nesting Monte Carlo Estimators

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

Comments To appear at International Conference on Machine Learning 2018

WSNet: Compact and Efficient Networks Through Weight Sampling

Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan

Comments To appear at ICML 2018

Discovering Discrete Latent Topics with Neural Variational Inference

Yishu Miao, Edward Grefenstette, Phil Blunsom

Comments ICML 2017

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian

Comments Accepted to ICML 2017

Journal ref Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia, PMLR 70:2681-2690, 2017

Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"

Shagun Sodhani, Vardaan Pahuja

Comments Submitted to Reproducibility in ML Workshop, ICML'18

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

Heinrich Jiang, Jennifer Jang, Samory Kpotufe

Comments ICML 2018. Code release: https://github.com/google/quickshift

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

Comments An extended abstract of this work appears in the Proceedings of the 34th International Conference on Machine Learning (ICML 2017)

Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results

Benjamin Paaßen

Comments Supplementary Materials and additional Results for the ICML 2018 paper Tree Edit Distance Learning via Adaptive Symbol Embeddings

Tropical Geometry of Deep Neural Networks

Liwen Zhang, Gregory Naitzat, Lek-Heng Lim

Comments 18 pages, 6 figures

Journal ref Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018

Hungarian Layer: Logics Empowered Neural Architecture

Han Xiao, Yidong Chen, Xiaodong Shi

Comments This is the draft submitting to ICML 2018. You could expect the final version, which is more perfect

Feedback-Based Tree Search for Reinforcement Learning

Daniel R. Jiang, Emmanuel Ekwedike, Han Liu

Comments 19 pages, to be presented at ICML 2018

Black-box Variational Inference for Stochastic Differential Equations

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

Comments V3 - revised based on ICML reviewer comments V2 - added acknowledgements and link to code

Do Outliers Ruin Collaboration?

Mingda Qiao

Comments Accepted to ICML 2018

TensOrMachine: Probabilistic Boolean Tensor Decomposition

Tammo Rukat, Chris C. Holmes, Christopher Yau

Comments To be published at ICML 2018

Is feature selection secure against training data poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

Journal ref Proc. of the 32nd ICML, Lille, France, 2015. JMLR: W&CP vol. 37

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations

Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio

Comments Under Review ICML 2018

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus

Comments 10 pages, 16 figures, submitted to ICML 2018

Deep saliency: What is learnt by a deep network about saliency?

Sen He, Nicolas Pugeault

Comments Accepted paper in 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning

Journal ref 2nd Workshop on Visualisation for Deep Learning, ICML 2017

Accuracy-Reliability Cost Function for Empirical Variance Estimation

Enrico Camporeale

Comments under review for ICML 2018

Being Robust (in High Dimensions) Can Be Practical

Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart

Comments Appeared in ICML 2017

Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, Frank Wood

Comments 10 pages. Updated from version at ICML 2016; includes code at http://github.com/tbrx/compiled-inference

Journal ref Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. In Proceedings of the 33rd International Conference on Machine Learning, JMLR W&CP 48: 3040-3049

Fibres of Failure: Classifying errors in predictive processes

Leo Carlsson, Gunnar Carlsson, Mikael Vejdemo-Johansson

Comments 10 pages, submitted to ICML 2018

A Denoising Loss Bound for Neural Network based Universal Discrete Denoisers

Taesup Moon

Comments submitted to ICML 2018

BachProp: Learning to Compose Music in Multiple Styles

Florian Colombo, Wulfram Gerstner

Comments Preliminary work. Under review by the 2018 International Conference on Machine Learning (ICML)

Soft-DTW: a Differentiable Loss Function for Time-Series

Marco Cuturi, Mathieu Blondel

Comments Published in ICML 2017

Consistent feature attribution for tree ensembles

Scott M. Lundberg, Su-In Lee

Comments presented at 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), Sydney, NSW, Australia

Machine Learning that Matters

Kiri Wagstaff

Comments ICML2012

Journal ref Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML), p. 529-536

Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary

Meg Pirrung, Nathan Hilliard, Artëm Yankov, Nancy O'Brien, Paul Weidert, Courtney D Corley, Nathan O Hodas

Comments ICML 2017 human-in-the-loop workshop

Predicting Adversarial Examples with High Confidence

Angus Galloway, Graham W. Taylor, Medhat Moussa

Comments Under review by the International Conference on Machine Learning (ICML)