Projects per year
Abstract
For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret. The adaptive regret of an algorithm on a time interval [t_1,t_2] is the loss of the algorithm there minus the loss of the best expert. Adaptive regret measures how well the algorithm approximates the best expert locally, and it is therefore somewhere between the classical regret (measured on all outcomes) and the tracking regret, where the algorithm is compared to a good sequence of experts.
We investigate two existing intuitive methods to derive algorithms with low adaptive regret, one based on specialist experts and the other based on restarts. Quite surprisingly, we show that both methods lead to the same algorithm, namely Fixed Share, which is known for its tracking regret. Our main result is a thorough analysis of the adaptive regret of Fixed Share. We obtain the exact worstcase adaptive regret for Fixed Share, from which the classical tracking bounds can be derived. We also prove that Fixed Share is optimal, in the sense that no algorithm can have a better adaptive regret bound.
We investigate two existing intuitive methods to derive algorithms with low adaptive regret, one based on specialist experts and the other based on restarts. Quite surprisingly, we show that both methods lead to the same algorithm, namely Fixed Share, which is known for its tracking regret. Our main result is a thorough analysis of the adaptive regret of Fixed Share. We obtain the exact worstcase adaptive regret for Fixed Share, from which the classical tracking bounds can be derived. We also prove that Fixed Share is optimal, in the sense that no algorithm can have a better adaptive regret bound.
Original language  English 

Title of host publication  Proceedings of the Twenty Third International Conference on Algorithmic Learning Theory 
Editors  Nader Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann 
Place of Publication  Berlin 
Publisher  Springer 
Pages  290  304 
Number of pages  15 
Volume  7568 
Publication status  Accepted/In press  2012 
Publication series
Name  Lecture Notes in Artificial Intelligence 

Publisher  Springer 
Keywords
 online learning
 adaptive regret
 Fixed Share
 specialist experts
Projects
 1 Finished

The Decision Theory of Hiring and Firing Advisors
Netherlands Organisation for Scientific Research
1/02/11 → 31/01/13
Project: Research