ICML 是 International Conference on Machine Learning的缩写，即国际机器学习大会。ICML如今已发展为由国际机器学习学会（IMLS）主办的年度机器学习国际顶级会议。
ICML2022共收到5630 篇投稿，其中，1117 篇被接收为short oral，118篇被接收为long oral。接收率为21.94%，与以往几年基本持平。本届大会共评选出15 篇杰出论文奖和 1 项时间检验奖。其中，复旦大学、上海交通大学、厦门大学、莱斯大学等多个华人团队的工作被评位杰出论文奖。ICML 2012 的一篇论文《Poisoning Attacks against Support Vector Machines》获得了时间检验奖。
获奖论文信息详见：https://icml.cc/virtual/2022/awards_detail
01. PAC-Bayesian Bounds on Rate-Efficient Classifiers
作者：Alhabib Abbas,Yiannis Andreopoulos
原文地址：
https://proceedings.mlr.press/v162/abbas22a/abbas22a.pdf
02. Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
作者：Momin Abbas,Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen
原文地址：
https://proceedings.mlr.press/v162/abbas22b/abbas22b.pdf
Github:
https://github.com/mominabbass/sharp-maml
03. An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn
作者：Emmanuel Abbe,Elisabetta Cornacchia, Jan Hazla, Christopher Marquis
原文地址：
https://proceedings.mlr.press/v162/abbe22a/abbe22a.pdf
04. Active Sampling for Min-Max Fairness
作者：Jacob D Abernethy,Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
原文地址：
https://proceedings.mlr.press/v162/abernethy22a/abernethy22a.pdf
Github:
https://github.com/amazon-research/active-sampling-for-minmax-fairness
05. Meaningfully debugging model mistakes using conceptual counterfactual explanations
作者：Abubakar Abid,Mert Yuksekgonul, James Zou
原文地址：
https://proceedings.mlr.press/v162/abid22a/abid22a.pdf
Github:
https://github.com/mertyg/debug-mistakes-cce
06. Batched Dueling Bandits
作者：Arpit Agarwal,Rohan Ghuge, Viswanath Nagarajan
原文地址：
https://proceedings.mlr.press/v162/agarwal22a/agarwal22a.pdf
07. Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models
作者：Abhineet Agarwal,Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu
原文地址：
https://proceedings.mlr.press/v162/agarwal22b/agarwal22b.pdf
Github：
https://github.com/yu-group/imodels-experiments
https://github.com/csinva/imodels
08. Deep equilibrium networks are sensitive to initialization statistics
作者：Atish Agarwala,Samuel S Schoenholz
原文地址：
https://proceedings.mlr.press/v162/agarwala22a/agarwala22a.pdf
09. Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
作者：Henrique Aguiar,Mauro Santos, Peter Watkinson, Tingting Zhu
原文地址：
https://proceedings.mlr.press/v162/aguiar22a/aguiar22a.pdf
10. On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
作者：Lucas Agussurja,Xinyi Xu, Bryan Kian Hsiang Low
原文地址：
https://proceedings.mlr.press/v162/agussurja22a/agussurja22a.pdf
Github：
https://github.com/XinyiYS/Parametric-Bayesian-Learning-Games
11. Individual Preference Stability for Clustering
作者：Saba Ahmadi,Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
原文地址：
https://proceedings.mlr.press/v162/ahmadi22a/ahmadi22a.pdf
Github：
https://github.com/amazon-research/ip-stability-for-clustering
12. Understanding the unstable convergence of gradient descent
作者：Kwangjun Ahn,Jingzhao Zhang, Suvrit Sra
原文地址：
https://proceedings.mlr.press/v162/ahn22a/ahn22a.pdf
13. Minimum Cost Intervention Design for Causal Effect Identification
作者：Sina Akbari,Jalal Etesami, Negar Kiyavash
原文地址：
https://proceedings.mlr.press/v162/akbari22a/akbari22a.pdf
其他材料：
https://media.icml.cc/Conferences/ICML2022/supplementary/akbari22a-supp.zip
GitHub：
https://github.com/sinaakbarii/min_cost_intervention
14. How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
作者：Ahmed Alaa,Boris Van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar
原文地址：
https://proceedings.mlr.press/v162/alaa22a/alaa22a.pdf
GitHub：
https://github.com/vanderschaarlab/evaluating-generative-models
15. A Natural Actor-Critic Framework for Zero-Sum Markov Games
作者：Ahmet Alacaoglu,Luca Viano, Niao He, Volkan Cevher
原文地址：
https://proceedings.mlr.press/v162/alacaoglu22a/alacaoglu22a.pdf
16. Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
作者：Mohammad Mahmudul Alam,Edward Raff, Tim Oates, James Holt
原文地址：
https://proceedings.mlr.press/v162/alam22a/alam22a.pdf
GitHub：
https://github.com/neuromorphiccomputationresearchprogram/connectionist-symbolic-pseudo-secrets
17. Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
作者：Lucas Nunes Alegre,Ana Bazzan, Bruno C. Da Silva
原文地址：
https://proceedings.mlr.press/v162/alegre22a/alegre22a.pdf
GitHub：
https://github.com/lucasalegre/sfols
18. Structured Stochastic Gradient MCMC
作者：Antonios Alexos,Alex J Boyd, Stephan Mandt
原文地址：
https://proceedings.mlr.press/v162/alexos22a/alexos22a.pdf
github：
https://github.com/ajboyd2/pytorch_lvi
19. XAI for Transformers: Better Explanations through Conservative Propagation
作者：Ameen Ali,Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf
原文地址：
https://proceedings.mlr.press/v162/ali22a/ali22a.pdf
其他材料：
https://media.icml.cc/Conferences/ICML2022/supplementary/ali22a-supp.zip
Github：
https://github.com/ameenali/xai_transformers
20. RUMs from Head-to-Head Contests
作者：Matteo Almanza,Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins
原文地址：
https://proceedings.mlr.press/v162/almanza22a/almanza22a.pdf
其他材料：
https://media.icml.cc/Conferences/ICML2022/supplementary/almanza22a-supp.zip
21. Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
作者：Uri Alon,Frank Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig
原文地址：
https://proceedings.mlr.press/v162/alon22a/alon22a.pdf
GitHub：
https://github.com/neulab/retomaton
22. Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
作者：Verónica Álvarez,Santiago Mazuelas, Jose A Lozano
原文地址：
https://proceedings.mlr.press/v162/alvarez22a/alvarez22a.pdf
其他材料：
https://media.icml.cc/Conferences/ICML2022/supplementary/alvarez22a-supp.zip
GitHub：
https://github.com/machinelearningbcam/amrc-for-concept-drift-icml-2022
23. Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
作者：Sebastian E Ament,Carla P Gomes
原文地址：
https://proceedings.mlr.press/v162/ament22a/ament22a.pdf
GitHub：
https://github.com/sebastianament/covariancefunctions.jl
24. Public Data-Assisted Mirror Descent for Private Model Training
作者：Ehsan Amid,Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta
原文地址：
https://proceedings.mlr.press/v162/amid22a/amid22a.pdf
25. On Last-Iterate Convergence Beyond Zero-Sum Games
作者：Ioannis Anagnostides,Ioannis Panageas, Gabriele Farina, Tuomas Sandholm
原文地址：
https://proceedings.mlr.press/v162/anagnostides22a/anagnostides22a.pdf
26. Online Algorithms with Multiple Predictions
作者：Keerti Anand,Rong Ge, Amit Kumar, Debmalya Panigrahi
原文地址：
https://proceedings.mlr.press/v162/anand22a/anand22a.pdf
27. Learning to Hash Robustly, Guaranteed
作者：Alexandr Andoni,Daniel Beaglehole
原文地址：
https://proceedings.mlr.press/v162/andoni22a/andoni22a.pdf
28. Set Based Stochastic Subsampling
作者：Bruno Andreis,Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang
原文地址：
https://proceedings.mlr.press/v162/andreis22a/andreis22a.pdf
29. Towards Understanding Sharpness-Aware Minimization
作者：Maksym Andriushchenko,Nicolas Flammarion
原文地址：
https://proceedings.mlr.press/v162/andriushchenko22a/andriushchenko22a.pdf
Github：
https://github.com/tml-epfl/understanding-sam
30. Fair and Fast k-Center Clustering for Data Summarization
作者：Haris Angelidakis,Adam Kurpisz, Leon Sering, Rico Zenklusen
原文地址：
https://proceedings.mlr.press/v162/angelidakis22a/angelidakis22a.pdf
其他材料：
https://media.icml.cc/Conferences/ICML2022/supplementary/angelidakis22a-supp.zip
