# ALT — International Conference on Algorithmic Learning Theory

- **Submission deadline**: 2026-10-12
- **Notification date**: 2027-01-05
- **Conference date**: 2027-03-09
- **Location**: Leiden, the Netherlands
- **Rankings**: CCF C · CORE B · QUALIS B1
- **Conference Partner Index**: 80.2/100 (ranked #195, confidence 1.00, algorithm 1.1) — how this is computed: https://www.myhuiban.com/ranking
- **Trackers**: 76
- **Attendees**: 24
- **Canonical page**: https://www.myhuiban.com/conference/419

## Acceptance history

| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| 2021 | 157 | 46 | 29.3% |
| 2020 | 128 | 38 | 29.7% |
| 2019 | 78 | 37 | 47.4% |
| 2018 | 95 | 33 | 34.7% |
| 2017 | 74 | 33 | 44.6% |
| 2008 | 46 | 31 | 67.4% |
| 2007 | 50 | 25 | 50% |
| 2006 | 53 | 24 | 45.3% |
| 2005 | 98 | 30 | 30.6% |
| 2004 | 91 | 29 | 31.9% |

## Past editions

| Year | Deadline | Conference date | Location |
|---|---|---|---|
| 2026 | 2025-10-02 | 2026-02-23 | Toronto, Ontario, Canada |

## Call for papers

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).

## Related conferences

- ADT — International Conference on Algorithmic Decision Theory — https://www.myhuiban.com/conference/1302
- SAGT — International Symposium on Algorithmic Game Theory — https://www.myhuiban.com/conference/4535
- ICC — International Conference on Communications — https://www.myhuiban.com/conference/318
- IJCNN — International Joint Conference on Neural Networks — https://www.myhuiban.com/conference/428
- TrustCom — International Conference on Trust, Security and Privacy in Computing and Communications — https://www.myhuiban.com/conference/1289

## Related journals

- Knowledge-Based Systems — https://www.myhuiban.com/journal/227
- Engineering Applications of Artificial Intelligence — https://www.myhuiban.com/journal/209
- Future Generation Computer Systems — https://www.myhuiban.com/journal/36
- Neurocomputing — https://www.myhuiban.com/journal/234
- Pattern Recognition Letters — https://www.myhuiban.com/journal/236

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Source: Conference Partner — https://www.myhuiban.com/conference/419 (rankings reproduced from CCF / ICORE / QUALIS; data cached up to 1 hour)