39th International Conference on Machine Learning

ICML 2022


Mathematical Optimization



Topics of interest include (but are not limited to):
General Machine Learning (active learning, clustering, online learning, ranking, reinforcement learning, semi-supervised learning, time series analysis, unsupervised learning, etc.)
Deep Learning (architectures, generative models, deep reinforcement learning, etc.)
Learning Theory (bandits, game theory, statistical learning theory, etc.)
Optimization (convex and non-convex optimization, matrix/tensor methods, sparsity, etc.)
Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness, etc.)
Applications (computational biology, crowdsourcing, healthcare, neuroscience, social good, climate science, etc.)