v272: Proceedings of ALT 2025

2026-06-04 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

Volume 272 presents the proceedings of The 36th International Conference on Algorithmic Learning Theory (ALT 2025), held from February 24-27, 2025, at Politecnico di Milano in Milan, Italy. Edited by Gautam Kamath and Po-Ling Loh, this extensive collection features a diverse array of theoretical machine learning research. Papers explore topics such as randomized exploration in linear bandits, generalization bounds for mixing processes, agnostic private density estimation for GMMs, and refining sample complexity in comparative learning. Further contributions delve into proper learnability, online correlation clustering, various multi-armed bandit problems, and differentially private multi-sampling. The volume also covers PAC-Bayesian generalization, online nonparametric regression, and the error dynamics of mini-batch gradient descent, offering deep insights into fundamental algorithmic learning challenges.

Key takeaway

For AI scientists and students researching foundational machine learning theory, reviewing Volume 272 offers a comprehensive overview of current advancements and open problems. You can identify emerging research directions in areas like generalization, privacy, and online learning, informing your own theoretical investigations. Consider exploring specific papers on bandit algorithms or PAC-Bayesian methods to deepen your understanding of their mathematical underpinnings and potential for future work.

Key insights

The 36th International Conference on Algorithmic Learning Theory (ALT 2025) proceedings compile diverse theoretical machine learning research.

Topics

• Algorithmic Learning Theory
• Generalization Bounds
• Bandit Algorithms
• Differential Privacy
• PAC Learning
• Online Learning
• Machine Learning Theory

Best for: Research Scientist, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.