The 37th Algorithmic Learning Theory conference (ALT 2026) will be held in Toronto, Canada on February 23-26, 2026. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to, the following list of topics.
- Design and analysis of learning algorithms.
- Classical foundations of learning theory: statistical, computational, algorithmic, and information-theoretic.
- Online learning and game theory.
- Optimization: convex, non-convex, new and old algorithms, their implicit biases, overparameterization, and so on.
- Different paradigms of learning: supervised, unsupervised, semi-supervised, active learning, reinforcement learning, and so on.
- All aspects of reinforcement learning: classical control-theoretic perspectives, modern uses such as LLM post-training, new algorithms, etc.
- Large language models, transformers, and all associated questions.
- Theoretical perspectives on trustworthy AI safety and AI safety: privacy, adaptive data analysis, fairness, alignment, and so on.
- Robustness: both classical perspectives (e.g. training data corruption), and modern perspectives (e.g. adversarial examples and LLM jailbreaks).
- Theoretical perspectives on deep learning: approximation, generalization, and optimization aspects of classical architectures such as shallow feedforward networks and simple RNNs, and modern architectures such as transformers.
- Core statistics topics: asymptotics, high-dimensional statistics, non-parametrics, causality, and so on.
- Learning with algebraic or combinatorial structure.
- Bayesian methods.
- Kernel methods.
- Interpretability and explainability.
- Learning with algorithmic constraints: distributed learning, communication and memory efficient learning, federated learning, streaming algorithms, and so on.
- Different learning modalities: time series, sequence-to-sequence mappings, graph data, and so on.
- Mathematical analysis of sampling methods, including diffusion models and other practical methods.
Despite the theoretical focus of the conference, authors are welcome to support their analysis with relevant empirical results. Accepted papers will be presented at the conference as a full-length talk, and published electronically in the Proceedings of Machine Learning Research (PMLR); see details below and in the eventual submission instructions.
Important Dates
Paper submission deadline: October 2, 2025, 11:59PM AoE.
Author feedback: November 17-23, 2025
Author notification: December 18, 2025
Conference Format
The conference will be in-person with no remote talks. At least one author of each accepted paper will be required to present their paper in-person at the conference. Emergencies such as VISA issues, travel safety issues, and medical issues will be evaluated on a case-by-case basis.
Authors of accepted papers will have the option of opting out of the proceedings in favor of a 1-page extended abstract, which will point to an open access archival version of the full paper reviewed for ALT.
Dual Submission Policy
Conferences: In general, submissions that are substantially similar to papers that have been previously published, accepted for publication, or submitted in parallel to other peer-reviewed conferences with proceedings may not be submitted to ALT.
Journals: Submissions that are substantially similar to papers that are already published in a journal at the time of submission may not be submitted to ALT.
Note: While the reviewing will be double-blind, authors are allowed and even encouraged to post their papers to arXiv.
Rebuttal Phase
After the initial review phase, authors will have the opportunity to respond to these initial reviews during a rebuttal phase. Authors are urged to provide succinct responses focusing on factual and technical aspects of the reviews. There is no need to reply at all—papers can be accepted and win paper awards without any rebuttal.
Awards
At most three awards will be given:
- An overall best paper award.
- A best student paper award, where the primary author must be a full-time student.
- A new *elegant paper award*, given to a submission that is exceptionally concise, well written, or otherwise elegant.
Contact
All questions about submissions should be emailed to the PC chairs at alt2026pc@gmail.com.