Beating Bandits in Gradually Evolving Worlds

author: Chia-Jung Lee, Institute of Information Science, Academia Sinica
published: Aug. 9, 2013,   recorded: June 2013,   views: 3193
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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 minimize the regret. In the full-information setting, the player after choosing her action can observe the whole loss function in that round, while in the bandit setting, the only information the player can observe is the loss value of that action. Designing such bandit algorithms appears challenging, as the best regret currently achieved for general convex loss functions is much higher than that in the full-information setting, while for strongly convex loss functions, there is even a regret lower bound which is exponentially higher than that achieved in the full-information setting. To aim for smaller regrets, we adopt a relaxed two-point bandit setting in which the player can play two actions in each round and observe the loss values of those two actions. Moreover, we consider loss functions parameterized by their deviation D, which measures how fast they evolve, and we study how regrets depend on D. We show that two-point bandit algorithms can in fact achieve regrets matching those in the full-information setting in terms of D. More precisely, for convex loss functions, we achieve a regret of O(D−−√), while for strongly convex loss functions, we achieve a regret of O(lnD), which is much smaller than the Ω(D−−√) lower bound in the traditional bandit setting.

See Also:

Download slides icon Download slides: colt2013_jung_lee_bandits_01.pdf (828.4 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: