22 search results - page 4 / 5 » High-Probability Regret Bounds for Bandit Online Linear Opti... |
LION
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Algorithm selection is typically based on models of algorithm performance learned during a separate offline training sequence, which can be prohibitively expensive. In r...
COLT
2004
Springer
14 years 27 days ago
2004
Springer
We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
CIMCA
2008
IEEE
14 years 2 months ago
2008
IEEE
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
We study the regret of an online learner playing a multi-round game in a Banach space B against an adversary that plays a convex function at each round. We characterize the minima...
NIPS
2007
13 years 9 months ago
2007
We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori k...