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Beating Bandits in Gradually Evolving Worlds

Published on Aug 09, 20133206 Views

Consider the online convex optimization problem, in which a player has to choose actions iteratively and suffers corresponding losses according to some convex loss functions, and the goal is to minimi

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Chapter list

Beating Bandits in Gradually Evolving Worlds00:00
Outline00:06
Online Learning: Routing (Example) - 100:23
Online Learning: Routing (Example) - 200:34
Online Learning: Model - 100:43
Online Learning: Model - 201:53
Online Learning: Model - 302:19
Online Learning: Model - 402:29
Full Information Setting02:53
Online Problems02:59
Previous Results - 103:17
Previous Result: Deviation04:02
Previous Results - 204:37
Bandit Setting04:45
Previous Results - 304:49
Previous Results - 404:57
Previous Results - 505:12
Previous Results - 605:43
Difficulties in Bandit Setting05:53
Approach for Bandit - 105:58
Approach for Bandit - 206:23
Approach for Bandit - 306:43
Estimation - 107:23
Estimation - 208:26
Estimation - 308:33
Estimation - 408:37
Estimation - 508:48
Estimation - 609:30
Another Issue: Exploration09:48
Two-Point Bandit Setting11:07
Two-Point Bandit [ADX10]11:17
Motivation11:38
Previous Results - 711:59
Previous Results - 812:10
Our Results 12:30
Main Algorithm - 112:45
Gradient Descent12:59
Full information Algorithm’s idea - 113:43
Approach for Bandit14:40
Observation 15:12
First Try 16:03
Main Algorithm - 216:13
Full information Algorithm’s idea - 216:22
Algorithm’s Idea - 116:49
Algorithm - 117:42
Algorithm’s Idea - 218:36
Algorithm - 218:55
Analysis19:10
Results19:36
Thank you !!19:50