Full papers
Accepted papers will be presented at the conference as full-length talks and published electronically in the Proceedings of Machine Learning Research (PMLR).
AI/CORE B
ALT 2027
International Conference on Algorithmic Learning Theory
ALT 2027 is the 38th International Conference on Algorithmic Learning Theory, dedicated to theoretical and algorithmic aspects of machine learning. It will be held in person in Leiden, the Netherlands, from March 9–12, 2027, and welcomes submissions on a broad range of topics from classical learning theory to modern challenges like large language models and AI safety.
Key deadlines
Verified September 9, 2026Full paper
October 13, 2026
AoE
Conference timeline
Submission and decisions
Full paperKey deadline
October 13, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
One-page extended abstracts
Authors of accepted papers may opt out of the proceedings and instead publish a one-page extended abstract pointing to an open-access archival version of the full paper reviewed for ALT.
Research areas in scope
01
Topics
Design and analysis of learning algorithmsClassical foundations of learning theory, including statistical, computational, algorithmic, and information-theoretic foundationsOnline learning, multi-agent learning, and game theoryOptimization, including convex and nonconvex methods, implicit bias, and overparameterizationLearning paradigms, including supervised, unsupervised, semi-supervised, active, and reinforcement learningReinforcement learning, including classical control-theoretic perspectives, modern applications such as LLM post-training, and new algorithmsLarge language models, transformers, and related theoretical questionsTheoretical perspectives on trustworthy AI and AI safety, including privacy, adaptive data analysis, fairness, and alignmentRobustness, including training-data corruption, adversarial examples, and LLM jailbreaksLearning under distribution shift, including domain adaptation and out-of-distribution generalizationTheoretical perspectives on deep learning, including approximation, generalization, and optimization for classical and modern architecturesStatistics, including asymptotics, high-dimensional statistics, nonparametric methods, and causalityLearning with algebraic or combinatorial structureBayesian methodsKernel methodsInterpretability and explainabilityLearning under algorithmic constraints, including distributed, communication-efficient, memory-efficient, federated, and streaming learningLearning with different data modalities, including time series, sequence-to-sequence mappings, and graph dataMathematical analysis of sampling methods, including diffusion models and other practical methods
Policies worth checking twice
- Submissions must not be substantially similar to papers previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings.
- Submissions must not be substantially similar to papers already published in a journal at the time of submission.
- Authors may post papers on arXiv; reviewers will not see author identities.
- At least one author of each accepted paper must present the paper in person at the conference.
- Exceptional circumstances (e.g., visa issues, travel-safety concerns, medical issues) may be considered on a case-by-case basis for remote presentation.
- Authors may use generative AI tools in preparing submissions but must remain fully responsible for content, including correctness, originality, attribution, and citations.
- Generative AI tools may not be listed as authors.
- Authors are strongly encouraged to disclose any substantive use of generative AI in a clearly labeled 'AI Disclosure' section, identifying the tool and explaining its use.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Official conference siteFor AuthorsCall for papersSubmission instructionsAccepted PapersCall for papers
Last verified September 9, 2026