ICML

Minimax Pareto Fairness: A Multi Objective Perspective

Natalia Martinez, Martin Bertran, Guillermo Sapiro

Journal ref International Conference on Machine Learning, 2020

Mathematical simulation of package delivery optimization using a combination of carriers

Valentyn M. Yanchuk, Andrii G. Tkachuk, Dmitry S. Antoniuk, Tetiana A. Vakaliuk, Anna A. Humeniuk

Journal ref International Conference on Machine Learning Techniques and NLP (MLNLP 2020), October 24-25, 2020, Sydney, Australia. Computer Science & Information Technology (CS & IT), Vol. 10, N.12, pp. 45-55

Inter-domain Deep Gaussian Processes

Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

Comments Published in Proceedings of the 37th International Conference on Machine Learning (ICML 2020)

CRICTRS: Embeddings based Statistical and Semi Supervised Cricket Team Recommendation System

Prazwal Chhabra, Rizwan Ali, Vikram Pudi

Comments 11 pages, 5 figures

Journal ref International Conference on Machine Learning Techniques and NLP (MLNLP 2020), October 24-25, 2020, Sydney, Australia

The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data

Fariborz Salehi, Ehsan Abbasi, Babak Hassibi

Comments ICML 2020 (submitted February 2020)

An Epidemiological Modelling Approach for Covid19 via Data Assimilation

Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang, Yike Guo

Comments Initial conference version accepted at International Conference of Machine Learning(ICML) workshop. Extended journal version was published in the European Journal of Epidemiology (https://doi.org/10.1007/s10654-020-00676-7). Please cite as accordingly

Contrastive Graph Neural Network Explanation

Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer

Comments ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+)

Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations

Tristan Sylvain, Linda Petrini, R Devon Hjelm

Comments ICML 2019 Workshop on Understanding and Improving General-ization in Deep Learning, Long Beach, California, 2019 Spotlight presentation. arXiv admin note: text overlap with arXiv:1912.12179

Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks

Srikanth Chandar, Anish Reddy, Muvazima Mansoor, Suresh Jamadagni

Comments 7 pages, 10 figures, Submitted to and accepted for presentation in the 19TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, Miami, Florida

Word Embeddings for Chemical Patent Natural Language Processing

Camilo Thorne, Saber Akhondi

Comments Extended version of an extended abstract presented (and reviewed) at the Latinx Workshop at ICML 2020

Private Outsourced Bayesian Optimization

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low

Comments 37th International Conference on Machine Learning (ICML 2020), Extended version with proofs, 27 pages

Collaborative Machine Learning with Incentive-Aware Model Rewards

Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low

Comments 37th International Conference on Machine Learning (ICML 2020), Extended version with proofs and additional experimental results, 17 pages

Investigating Saturation Effects in Integrated Gradients

Vivek Miglani, Narine Kokhlikyan, Bilal Alsallakh, Miguel Martin, Orion Reblitz-Richardson

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

Towards a General Theory of Infinite-Width Limits of Neural Classifiers

Eugene A. Golikov

Comments 27 pages, 7 figures, accepted to ICML'2020

Handling the Positive-Definite Constraint in the Bayesian Learning Rule

Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan

Comments Fixed typos and updated the abstract (ICML 2020)

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

Comments 52 pages, 11 figures; Preliminary version in International Conference on Machine Learning 2018

Journal ref Journal of Machine Learning Research 21 (2020) 1-54

The Need for Standardized Explainability

Othman Benchekroun, Adel Rahimi, Qini Zhang, Tetiana Kodliuk

Comments Accepted in 2nd ICML 2020 Workshop on Human in the Loop Learning

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans

Comments 25 pages, ICML 2020

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits

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

Comments ICML 2020

Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, Hailin Jin

Comments A significant extension of this paper is accepted by TPAMI-20. A conference version of this paper is accepted by ECCV-18. A shorter version of this paper is accepted by ICML-18 PiMLAI workshop

Proper Network Interpretability Helps Adversarial Robustness in Classification

Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel

Comments 22 pages, 9 figures, Published at ICML 2020

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

Di Wang, Hanshen Xiao, Srini Devadas, Jinhui Xu

Comments Published in ICML 2020

CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images

Linchen Qian, Jiasong Chen, Timur Urakov, Weiyong Gu, Liang Liang

Comments This paper is accepted by 19th IEEE International Conference on Machine Learning and Applications (ICMLA2020)

Real-Time Optimisation for Online Learning in Auctions

Lorenzo Croissant, Marc Abeille, Clément Calauzènes

Comments International Conference on Machine Learning 2020, Jul 2020, Vienna, Austria

Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks

Theodoros Tsiligkaridis

Comments preliminary version presented at ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning, submitted

A Differentiable Newton Euler Algorithm for Multi-body Model Learning

Michael Lutter, Johannes Silberbauer, Joe Watson, Jan Peters

Comments ICML 2020 Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning

Weakly-Supervised Disentanglement Without Compromises

Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

Comments We updated the description of the generation of the dataset compared to the ICML version

Journal ref ICML 2020

Privately detecting changes in unknown distributions

Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang

Journal ref Proceedings of the International Conference on Machine Learning (2020) Pages 958-968

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

Comments ICML 2020

Stochastic Variance-Reduced Hamilton Monte Carlo Methods

Difan Zou, Pan Xu, Quanquan Gu

Comments 23 pages, 3 figures, 4 tables. In ICML 2018