35 search results - page 1 / 7 » Online Multiple Instance Learning with No Regret |
CVPR
2010
IEEE
14 years 1 months ago
2010
IEEE
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been u...
COLT
2010
Springer
13 years 3 months ago
2010
Springer
We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of ou...
SOFSEM
2010
Springer
14 years 2 months ago
2010
Springer
Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single de...
COLT
2010
Springer
13 years 3 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...
CORR
2011
Springer
13 years 9 days ago
2011
Springer
In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K...