263 search results - page 5 / 53 » Regret Bounds for Prediction Problems |
COLT
2008
Springer
13 years 9 months ago
2008
Springer
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
COLT
2004
Springer
13 years 11 months ago
2004
Springer
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data ana...
SODA
2010
ACM
14 years 4 months ago
2010
ACM
The online learning problem requires a player to iteratively choose an action in an unknown and changing environment. In the standard setting of this problem, the player has to ch...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...