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ICML 2025 Papers: Explore a comprehensive collection of cutting-edge research papers presented at ICML 2025, the premier conference in machine learning. Stay up to date with the latest breakthroughs in deep learning, generative models, optimization, AI theory, reinforcement learning, graph learning, causality, scalable machine learning systems, interpretability, fairness, and multimodal foundation models. Code implementations included. :star: the repository for advancing machine learning research and development!

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Other collections of the best AI conferences

Conference	Year
	2023	2024	2025
Computer Vision (CV)
CVPR
ICCV
ECCV
WACV	:heavy_minus_sign:
FG	:heavy_minus_sign:
Speech/Signal Processing (SP/SigProc)
ICASSP
INTERSPEECH
ISMIR		:heavy_minus_sign:	:heavy_minus_sign:
Natural Language Processing (NLP)
EMNLP
Machine Learning (ML)
AAAI	:heavy_minus_sign:
ICLR	:heavy_minus_sign:
ICML	:heavy_minus_sign:
NeurIPS	:heavy_minus_sign:

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Contributors

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Papers-2025

Section	Papers
Main
Orals
Alignment and Agents
Positions: Better Ways to Do Machine Learning
Applications in Computer Vision
Learning Dynamics
Theory and Phenomenology
Diffusion Models
Positions: AI Regulation and Safety
Reinforcement Learning
Efficient ML
Optimal Transport
Reasoning
Representations
Data-Centric ML
3D Optimization
Causality and Domain Generalization
Positions: Generative AI Evaluation
Privacy and Uncertainty Quantification
Applications in Science and Language
Algorithms
Safety and Security
Deep Learning Algorithms
Probablistic Models
Applications in Math and Physics
Learning Theory
Applications in Agents and Coding
Deep Learning Architectures
Evaluation
Social and Economic Perspectives

Track	Section	Papers
Main
Posters
Applications	Chemistry, Physics, and Earth Sciences		Will soon be added
	Computer Vision
	Energy
	Everything Else
	Health / Medicine
	Language, Speech and Dialog
	Neuroscience, Cognitive Science
	Robotics
	Social Sciences
	Time Series
Deep Learning	Algorithms
	Attention Mechanisms
	Everything Else		Will soon be added
	Foundation Models
	Generative Models and Autoencoders
	Graph Neural Networks
	Large Language Models
	Other Representation Learning
	Robustness
	Self-Supervised Learning
	Sequential Models, Time Series
	Theory
General Machine Learning	Causality
	Clustering
	Evaluation
	Everything Else
	Hardware and Software
	Kernel Methods
	Online Learning, Active Learning and Bandits
	Representation Learning
	Scalable Algorithms
	Sequential, Network, and Time Series Modeling
	Supervised Learning
	Transfer, Multitask and Meta-Learning
	Unsupervised and Semi-Supervised Learning
Optimization	Convex
	Discrete and Combinatorial Optimization
	Everything Else
	Large Scale, Parallel and Distributed
	Non-Convex
	Stochastic
	Zero-Order and Black-Box Optimization
Position	Data
	Methodology
	Risk Safety Policy
	Social Ethical Env Impact
	Understanding
Probabilistic Methods	Bayesian Models and Methods
	Everything Else
	Gaussian Processes
	Graphical Models
	Monte Carlo and Sampling Methods
	Spectral Methods
	Structure Learning
	Variational Inference
Reinforcement Learning	Batch/Offline
	Deep RL
	Everything Else
	Inverse
	Multi-Agent
	Online
	Planning
	Policy Search
Social Aspects	Accountability, Transparency, and Interpretability
	Alignment
	Fairness
	Privacy
	Robustness
	Safety
	Security
Theory	Active Learning and Interactive Learning
	Deep Learning
	Domain Adaptation and Transfer Learning
	Everything Else
	Game Theory
	Learning Theory
	Online Learning and Bandits
	Optimization
	Probabilistic Methods
	Reinforcement Learning and Planning

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Key Terms

Will soon be added

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Star History