v49: COLT 2016 Proceedings
Summary
Volume 49 presents the proceedings of the 29th Annual Conference on Learning Theory (COLT 2016), held from June 23-26, 2016, at Columbia University, New York, and edited by Vitaly Feldman, Alexander Rakhlin, and Ohad Shamir. The collection features a preface, numerous regular papers, and a dedicated section on "Open Problems" that highlight current challenges in the field. Key research areas explored include various bandit problems, such as contextual, multi-armed, and dueling bandits, alongside advanced optimization techniques like non-convex, semi-definite, and online optimization. Papers also delve into theoretical aspects of deep learning, including expressive power and the benefits of network depth, as well as reinforcement learning for Partially Observable Markov Decision Processes (POMDPs). Further topics encompass community detection in networks, compressed sensing, and fundamental learning theory concepts like VC dimension and regret bounds.
Key takeaway
The 29th Conference on Learning Theory (COLT 2016) proceedings compile significant theoretical advances across machine learning, optimization, and statistical inference. Papers address topics like online learning, bandit algorithms, non-convex optimization, deep learning expressivity, and community detection, complemented by a dedicated section on open problems. This volume offers critical foundational insights and outlines key research directions for academics and advanced practitioners in AI and ML.
Topics
- Learning Theory
- Multi-Armed Bandits
- Optimization Algorithms
- Reinforcement Learning
- Deep Learning Theory
Best for: AI Scientist, Research Scientist
Related on AIssential
Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.