COLT 2015 - Paris
28th Annual Conference on Learning Theory (COLT), Paris 2015
Learning Theory is a research field devoted to studying the design and analysis of machine learning algorithms. In particular, such algorithms aim at making accurate predictions or representations based on observations.
The emphasis in COLT is on rigorous mathematical analysis using techniques from various connected fields such as probability, statistics, optimization, information theory and geometry. While theoretically rooted, learning theory puts a strong emphasis on efficient computation as well.
For more information visit the COLT 2015 website.
Invited Talks | ||||
Computational Learning | ||||
Optimization I | ||||
On-Line Learning & Bandits I | ||||
Classification | ||||
Unsupervised Learning | ||||
Optimization, Online Learning, Loss Functions | ||||
Estimation, Generative Models | ||||
On-Line Learning & Bandits II | ||||
Open Problems Session | ||||
Probabilistic Models and Reinforcement Learning | ||||
Regression | ||||
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