Conference Information

ALT 2027: International Conference on Algorithmic Learning Theory

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Submission Date:
2026-10-12 Due in 7 days
Notification Date:
2027-01-05
Conference Date:
2027-03-09
Location:
Leiden, the Netherlands
Years:
CCF: C   ICORE: B   QUALIS: B1   Viewed: 108387   Tracked: 77   Attend: 25

Conference Partner Index (CP-I)

80.5 / 100
Ranked #194 of 5,693 conferences · Top 4%

#11 of 142 in Theory & Algorithms #18 of 742 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
78
Submission selectivity (20%)
70
Editions held (20%)
99
Community attention (10%)
68
Public record completeness (15%)
85

Inputs used: Listed as CCF C, ICORE B, QUALIS B1 · Acceptance rate: 37.2% (mean of 5 editions on file) · Editions on record: 38 · Researchers following it here: 77 · Researchers who opened this page in the past 24 months: 31

Missing from the public record: Best-paper records (+2.3)
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Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-04

Call For Papers

ALT 2027 (International Conference on Algorithmic Learning Theory) is a CCF C / ICORE B / QUALIS B1 conference held in Leiden, the Netherlands on 2027-03-09. The paper submission deadline is 2026-10-12. Acceptance notifications are sent on 2027-01-05.

The 38th International Conference on Algorithmic Learning Theory (ALT 2027) will be held March 9–12, 2027, in Leiden, the Netherlands. The conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions on new or existing learning problems, including but not limited to the following topics: Design and analysis of learning algorithms Classical foundations of learning theory, including statistical, computational, algorithmic, and information-theoretic foundations Online learning, multi-agent learning, and game theory Optimization, including convex and nonconvex methods, implicit bias, and overparameterization Learning paradigms, including supervised, unsupervised, semi-supervised, active, and reinforcement learning Reinforcement learning, including classical control-theoretic perspectives, modern applications such as LLM post-training, and new algorithms Large language models, transformers, and related theoretical questions Theoretical perspectives on trustworthy AI and AI safety, including privacy, adaptive data analysis, fairness, and alignment Robustness, including training-data corruption, adversarial examples, and LLM jailbreaks Learning under distribution shift, including domain adaptation and out-of-distribution generalization Theoretical perspectives on deep learning, including approximation, generalization, and optimization for classical and modern architectures Statistics, including asymptotics, high-dimensional statistics, nonparametric methods, and causality Learning with algebraic or combinatorial structure Bayesian methods Kernel methods Interpretability and explainability Learning under algorithmic constraints, including distributed, communication-efficient, memory-efficient, federated, and streaming learning Learning with different data modalities, including time series, sequence-to-sequence mappings, and graph data 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 full-length talks and published electronically in the Proceedings of Machine Learning Research (PMLR).
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Acceptance Ratio

Average acceptance rate: 41.1% over 10 years (2004–2021).

YearSubmittedAcceptedAccepted(%)
20211574629.3%
20201283829.7%
2019783747.4%
2018953334.7%
2017743344.6%
2008463167.4%
2007502550%
2006532445.3%
2005983030.6%
2004912931.9%

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