v336: Proceedings of COLT 2026

2026-06-30 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences, Data Science & Analytics · Depth: Expert, extended

Summary

Volume 336 presents the proceedings of the Thirty Ninth Annual Conference on Learning Theory (COLT 2026), held from July 29-3, 2026, in San Diego, California. Edited by Steve Hanneke and Tor Lattimore, this volume compiles a preface, numerous original research papers, and several open problems in the field of learning theory. The original papers cover a vast array of topics, including theoretical aspects of robust regression, k-SAT, independence testing, structured matrix learning, various optimization algorithms like SGD and Newton methods, quantum learning, active learning, and online learning. Other significant areas explored include diffusion models, neural network theory, multi-armed bandits, differential privacy, graph theory, and the fundamental limits of learning. The "Open Problems" section highlights key unanswered questions concerning tensor decomposition, deep learning's distribution dependence, and the impact of differential privacy on PAC learning.

Key takeaway

For research scientists and AI students tracking the forefront of theoretical machine learning, you should review these proceedings to identify emerging trends and foundational challenges. This volume offers a comprehensive snapshot of current research, informing your understanding of topics like robust optimization, quantum learning, and privacy-preserving algorithms. Consider exploring the "Open Problems" section to pinpoint areas ripe for new research contributions.

Key insights

The Thirty Ninth Annual Conference on Learning Theory proceedings highlight diverse advancements in theoretical machine learning research.

Topics

• Theoretical Machine Learning
• Optimization Algorithms
• Quantum Learning
• Differential Privacy
• Multi-Armed Bandits
• Neural Network Theory
• Graph Learning

Best for: AI Scientist, Research Scientist, AI Student

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