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ALT 2027: International Conference on Algorithmic Learning Theory

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截稿日期:
2026-10-12 还有 7 天
通知日期:
2027-01-05
会议日期:
2027-03-09
会议地点:
Leiden, the Netherlands
届数:
CCF: C   ICORE: B   QUALIS: B1   浏览: 108393   关注: 77   参加: 25

会伴指数 (CP-I)

80.5 / 100
全站第 194 名 / 共 5,693 个会议 · 前 4%

理论与算法 第 11 / 142 人工智能与机器学习 第 18 / 742

学术认可 (35%)
78
投稿选择性 (20%)
70
会议传承 (20%)
99
社区关注 (10%)
68
资料公开度 (15%)
85

用到的输入: 收录等级：CCF C, ICORE B, QUALIS B1 · 录用率：37.2%（有记录的 5 届的均值） · 有据可查的届次：38 · 在会伴关注它的研究者：77 人 · 过去 24 个月打开过本页的研究者：31 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-04

征稿

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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录用率

平均录用率: 41.1% 10 年间 (2004–2021).

年份提交数录用数录用率(%)
20211574629.3%
20201283829.7%
2019783747.4%
2018953334.7%
2017743344.6%
2008463167.4%
2007502550%
2006532445.3%
2005983030.6%
2004912931.9%

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