ICML

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama

Comments ICML 2020 final version

The Implicit and Explicit Regularization Effects of Dropout

Colin Wei, Sham Kakade, Tengyu Ma

Comments Published in ICML 2020. Code available at https://github.com/cwein3/dropout-analytical

Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow

S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, Hideki Asai

Comments Paper published in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

Explaining Creative Artifacts

Lav R. Varshney, Nazneen Fatema Rajani, Richard Socher

Comments 2020 Workshop on Human Interpretability in Machine Learning (WHI), at ICML 2020

On the relationship between class selectivity, dimensionality, and robustness

Matthew L. Leavitt, Ari S. Morcos

Comments Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning

Training Binary Neural Networks through Learning with Noisy Supervision

Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu

Comments ICML 2020

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination

Shauharda Khadka, Somdeb Majumdar, Santiago Miret, Stephen McAleer, Kagan Tumer

Comments Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 108, 2020

Journal ref Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020

Proportionally Fair Clustering

Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala

Comments To appear in ICML 2019

Collaborative Evolutionary Reinforcement Learning

Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer

Comments Added link to public Github repo. Minor editorial changes. Order of authors modified to reflect ICML submission

Journal ref Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019

Fake Reviews Detection through Analysis of Linguistic Features

Faranak Abri, Luis Felipe Gutierrez, Akbar Siami Namin, Keith S. Jones, David R. W. Sears

Comments The pre-print of a paper to appear in the proceedings of the IEEE International Conference on Machine Learning Applications (ICMLA 2020), 11 pages, 3 figures, 5 tables

Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition

Varsha Nair, Moitrayee Chatterjee, Neda Tavakoli, Akbar Siami Namin, Craig Snoeyink

Comments Pre-print of a paper to appear in the proceedings of the IEEE International Conference on Machine Learning Applications (ICMLA 2020), 10 pages, 9 figures, 1 table

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang

Journal ref 37th International Conference on Machine Learning, 2020

MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching

Rahul Mishra, Piyush Yadav, Remi Calizzano, Markus Leippold

Comments Accepted paper; IEEE 2020 International Conference on Machine Learning and Applications (ICMLA)

Bio-Inspired Hashing for Unsupervised Similarity Search

Chaitanya K. Ryali, John J. Hopfield, Leopold Grinberg, Dmitry Krotov

Comments Accepted for publication in ICML 2020

Journal ref Proceedings of the International Conference on Machine Learning, 2020, pp.8739-8750

Stochastically Dominant Distributional Reinforcement Learning

John D. Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot

Comments Accepted to the 2020 International Conference on Machine Learning

Improving Generative Imagination in Object-Centric World Models

Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, Sungjin Ahn

Comments Accepted in ICML 2020

Representations for Stable Off-Policy Reinforcement Learning

Dibya Ghosh, Marc G. Bellemare

Comments ICML 2020

Online Learning with Imperfect Hints

Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Comments appeared in ICML 2020

Inference under Information Constraints II: Communication Constraints and Shared Randomness

Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi

Comments To appear in IEEE Transactions on Information Theory. An abridged version of this work appeared in the 2019 International Conference on Machine Learning (ICML)

Geometric Matrix Completion: A Functional View

Abhishek Sharma, Maks Ovsjanikov

Comments Accepted at GRL workshop, ICML'20. Code: \url{https://github.com/Not-IITian/functional-matrix-completion}

Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering

Joris Guerin, Anne Magaly de Paula Canuto, Luiz Marcos Garcia Goncalves

Comments 8 pages, 11 figures, 1 table Accepted as a full paper at ICMLA 2020 (19th IEEE International Conference On Machine Learning And Applications)

Non-Pharmaceutical Intervention Discovery with Topic Modeling

Jonathan Smith, Borna Ghotbi, Seungeun Yi, Mahboobeh Parsapoor

Comments ML for Global Health (ICML 2020 Workshop)

On $\ell_p$-norm Robustness of Ensemble Stumps and Trees

Yihan Wang, Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh

Comments ICML 2020

Scalable Nearest Neighbor Search for Optimal Transport

Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Comments ICML 2020

On Efficient Constructions of Checkpoints

Yu Chen, Zhenming Liu, Bin Ren, Xin Jin

Journal ref International Conference on Machine Learning, 2020

Graph neural induction of value iteration

Andreea Deac, Pierre-Luc Bacon, Jian Tang

Comments ICML GRL+ 2020

Small Data, Big Decisions: Model Selection in the Small-Data Regime

Jorg Bornschein, Francesco Visin, Simon Osindero

Journal ref Proceedings of the International Conference on Machine (ICML 2020)

Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)

Gautam Raj Mode, Khaza Anuarul Hoque

Comments This is the extended version of the paper "Crafting Adversarial Examples for Deep Learning Based Prognostics" accepted for publication in the IEEE International Conference on Machine Learning and Applications (ICMLA 2020)

ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness

Anindya Sarkar, Anirudh Sunder Raj, Raghu Sesha Iyengar

Comments 8 pages, 5 figures, International Conference on Machine Learning and Applications

Stabilizing the Lottery Ticket Hypothesis

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin

Comments This article has been subsumed by "Linear Mode Connectivity and the Lottery Ticket Hypothesis" (arXiv:1912.05671, ICML 2020). Please read/cite that article instead