Foundations and Trends in Machine Learning

Aims & Scope✦ Inferred from recent articles

This journal publishes research on machine learning, covering topics such as preference learning, continual learning, AI for science, meta-reinforcement learning, generalization bounds, hyperparameter optimization, time-to-event prediction, automated deep learning, causal fairness analysis, PAC-Bayesian bounds, mean-field spin glasses, data-efficient reinforcement learning, amortized optimization, conformal prediction, Riemannian geometry, graph neural networks for NLP, model-based reinforcement learning, discrete splines, risk-sensitive reinforcement learning, approximate message passing algorithms, learning in repeated auctions, and dynamical variational autoencoders.

AI-summarised from recent articles · verify on publisher page →

Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.

Source: OpenAlex · Note: citations accumulate over time so older years appear higher

SJR Quartile by Discipline

Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.

Subject Classification