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ICML 2023 Papers

International Conference on Machine Learning

2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015

Decision breakdown

1,865papers

Counted from the official paper list; may differ slightly from the number the conference announced.

Oral
1558.3%
Poster
1,71091.7%

Top 10 topics (click to filter)

Reinforcement Learning297 Reinforcement Learning paper trends, 2023–2026
Deep learning · generative models and autoencoders286 Deep learning · generative models and autoencoders paper trends, 2023–2026
Simulation for Driving218 Simulation for Driving paper trends, 2023–2026
Datasets and Benchmarks166 Datasets and Benchmarks paper trends, 2023–2026
General ML · unsupervised and semisupervised learning157 General ML · unsupervised and semisupervised learning paper trends, 2023–2026
Deep learning · graph neural networks148 Deep learning · graph neural networks paper trends, 2023–2026
Multimodal LLMs146 Multimodal LLMs paper trends, 2023–2026
Diffusion Models109 Diffusion Models paper trends, 2023–2026
Reasoning Models109 Reasoning Models paper trends, 2023–2026
Alignment and Safety101 Alignment and Safety paper trends, 2023–2026

Trends for all 207 research topics

All1,865 Oral155 Poster1,710

TitleTypeTopic

A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

Poster

Deep learning · large language models Deep learning · generative models and autoencoders

Minimal Width for Universal Property of Deep RNN

Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang

Poster

Deep learning · large language models Deep learning · generative models and autoencoders

Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments

Haixu Ma, Donglin Zeng, Yufeng Liu

Poster

Deep learning · large language models Deep learning · generative models and autoencoders

Towards Learning to Imitate from a Single Video Demonstration

Glen Berseth, Florian Golemo, Christopher Pal

Poster

Reinforcement Learning Learning to Rank

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock

Poster

Deep learning · large language models Deep learning · generative models and autoencoders

The multimarginal optimal transport formulation of adversarial multiclass classification

Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim

Poster

Mixture of Experts Adversarial Robustness

Sampling random graph homomorphisms and applications to network data analysis

Hanbaek Lyu, Facundo Memoli, David Sivakoff

Poster

Applications · chemistry physics and earth sciences Applications · neuroscience and cognitive science

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach

Yanwei Jia, Xun Yu Zhou

Poster

Reinforcement Learning Deep learning · generative models and autoencoders

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima

Brian Swenson, Ryan Murray, H. Vincent Poor, Soummya Kar

Poster

Distributed Training Sim-to-Real

Weakly Supervised Disentangled Generative Causal Representation Learning

Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang

Poster

Deep learning · generative models and autoencoders Deep learning · self supervised learning

Underspecification Presents Challenges for Credibility in Modern Machine Learning

Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Maksym Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

Poster

General ML · supervised learning General ML · unsupervised and semisupervised learning

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism

Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

Poster

Alignment and Safety AI Agents

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks

Aleksandr Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli

Poster

Deep learning · graph neural networks Graph Neural Networks

Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials

Daiqi Gao, Yufeng Liu, Donglin Zeng

Poster

Adaptive Methods Mixture of Experts

Data-Derived Weak Universal Consistency

Narayana Prasad Santhanam, Venkatachalam Anantharam, Wojciech Szpankowski

Poster

Synthetic Data Data Cleaning

Cluster-Specific Predictions with Multi-Task Gaussian Processes

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

Poster

Gaussian Processes Multi-Task Ranking

On the Convergence Rates of Policy Gradient Methods

Lin Xiao

Poster

Deep learning · probabilistic methods Adaptive Methods

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence

Julie Nutini, Issam Laradji, Mark Schmidt

Poster

Alignment and Safety Social aspects · accountability transparency and interpretability

Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks

El Mehdi Achour, Francois Malgouyres, Franck Mamalet

Poster

Deep learning · graph neural networks Graph Neural Networks

Constraint Reasoning Embedded Structured Prediction

Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue

Poster

Reasoning Models Click-Through Prediction