2010
NeurIPS
NeurIPS 2010
Random Walk Approach to Regret Minimization
Abstract
We propose a computationally efficient random walk on a convex body which rapidly mixes to a time-varying Gibbs distribution. In the setting of online convex optimization and repeated games, the algorithm yields low regret and presents a novel efficient method for implementing mixture forecasting strategies.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Game Theory
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Hot Topic Early Bird
- regret minimization
Authors
Topics
Artificial Intelligence > Core AI > Game AI
Machine Learning > Optimization & Theory > Optimization
Mathematics & Optimization > Optimization > Stochastic Methods
Mathematics & Optimization > Optimization > Online Algorithms
Machine Learning > Learning Types > Online Learning
Machine Learning > Optimization & Theory > Online Algorithms
Machine Learning > Optimization & Theory > Stochastic Methods
Mathematics & Optimization > Optimization > Game Theory
Artificial Intelligence > Core AI > Game Theory
Machine Learning > Learning Types > Exploration-Exploitation