ICML

Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning

Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu

Comments Accepted at ICML 2023

On the Stepwise Nature of Self-Supervised Learning

James B. Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht

Comments 9 pages (main text) + 14 pages (refs + appendices). ICML '23

Are Diffusion Models Vulnerable to Membership Inference Attacks?

Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu

Comments To appear in ICML 2023

Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary

Alexander Lindermayr, Nicole Megow, Martin Rapp

Comments To appear at ICML 2023

End-to-End Full-Atom Antibody Design

Xiangzhe Kong, Wenbing Huang, Yang Liu

Comments Accepted to ICML 2023

WL meet VC

Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe

Comments Accepted at ICML 2023. arXiv admin note: text overlap with arXiv:2206.11168

Learning Control by Iterative Inversion

Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar

Comments ICML 2023. Videos available at https://sites.google.com/view/iter-inver

Journal ref Proceedings of the 40th International Conference on Machine Learning (ICML 2023)

Men Also Do Laundry: Multi-Attribute Bias Amplification

Dora Zhao, Jerone T. A. Andrews, Alice Xiang

Comments Accepted at ICML 2023

InfoOT: Information Maximizing Optimal Transport

Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis

Journal ref ICML 2023

Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions

Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery

Comments 20 pages

Journal ref ICML 2023

Integrating Prior Knowledge in Contrastive Learning with Kernel

Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori

Comments ICML 2023

Which Invariance Should We Transfer? A Causal Minimax Learning Approach

Mingzhou Liu, Xiangyu Zheng, Xinwei Sun, Fang Fang, Yizhou Wang

Comments Accepted version of ICML-23

Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression

Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, Baharan Mirzasoleiman

Comments to appear at ICML 2023

Fast Online Node Labeling for Very Large Graphs

Baojian Zhou, Yifan Sun, Reza Babanezhad

Comments 40 pages,17 figures, ICML 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan

Comments Accepted paper at ICML

Efficient Parametric Approximations of Neural Network Function Space Distance

Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Grosse

Comments 18 pages, 5 figures, ICML 2023

Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu

Comments Accepted by ICML 2023, 58 pages

On the Robustness of Randomized Ensembles to Adversarial Perturbations

Hassan Dbouk, Naresh R. Shanbhag

Comments Published as a conference paper in ICML 2023

Hierarchical Imitation Learning with Vector Quantized Models

Kalle Kujanpää, Joni Pajarinen, Alexander Ilin

Comments To appear at ICML 2023

Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming

Chunlin Sun, Shang Liu, Xiaocheng Li

Comments to be published in ICML 2023

Perturbation Analysis of Neural Collapse

Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

Comments ICML 2023

Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search

Xin Qiu, Risto Miikkulainen

Comments Published in ICML 2023

CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family

S Ashwin Hebbar, Viraj Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath

Comments 23 pages, 23 figures. ICML 2023

Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data

Yubo Zhuang, Xiaohui Chen, Yun Yang

Comments Accepted to ICML 2023

ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

Jintai Chen, Kuanlun Liao, Kun Wei, Haochao Ying, Danny Z. Chen, Jian Wu

Journal ref In International Conference on Machine Learning, 3360--3370, (2022), PMLR

Anchor Sampling for Federated Learning with Partial Client Participation

Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao

Comments ICML 2023

Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration

Theodoros Tsiligkaridis, Athanasios Tsiligkaridis

Comments Accepted to IJCNN 2023. Preliminary version accepted to ICML 2021 Uncertainty & Robustness in Deep Learning Workshop

GC-Flow: A Graph-Based Flow Network for Effective Clustering

Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen

Comments ICML 2023. Code is available at https://github.com/xztcwang/GCFlow

Optimizing NOTEARS Objectives via Topological Swaps

Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar

Comments 39 pages, 12 figures, ICML 2023

Feature Expansion for Graph Neural Networks

Jiaqi Sun, Lin Zhang, Guangyi Chen, Kun Zhang, Peng XU, Yujiu Yang

Comments Accepted by ICML'23