Sciweavers

23 search results - page 1 / 5
» Online Optimization in X-Armed Bandits
Sort
View
COLT
2008
Springer
13 years 5 months ago
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O ( T) regret. The setting is a natural general...
Jacob Abernethy, Elad Hazan, Alexander Rakhlin
NIPS
2007
13 years 5 months ago
The Price of Bandit Information for Online Optimization
In the online linear optimization problem, a learner must choose, in each round, a decision from a set D ⊂ Rn in order to minimize an (unknown and changing) linear cost function...
Varsha Dani, Thomas P. Hayes, Sham Kakade
SDM
2007
SIAM
167views Data Mining» more  SDM 2007»
13 years 5 months ago
Bandits for Taxonomies: A Model-based Approach
We consider a novel problem of learning an optimal matching, in an online fashion, between two feature spaces that are organized as taxonomies. We formulate this as a multi-armed ...
Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabar...
CP
2006
Springer
13 years 7 months ago
A Simple Distribution-Free Approach to the Max k-Armed Bandit Problem
The max k-armed bandit problem is a recently-introduced online optimization problem with practical applications to heuristic search. Given a set of k slot machines, each yielding p...
Matthew J. Streeter, Stephen F. Smith
CORR
2011
Springer
210views Education» more  CORR 2011»
12 years 10 months ago
Online Learning of Rested and Restless Bandits
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...
Cem Tekin, Mingyan Liu