@inproceedings{ae6a037110254c26b7284a28ab76fbf5,
title = "Adaptive algorithms and data-dependent guarantees for bandit convex optimization",
abstract = "We present adaptive algorithms with strong datadependent regret guarantees for the problem of bandit convex optimization. In the process, we develop a general framework from which the main previous results in this setting can be recovered. The key method is the introduction of adaptive regularization. By appropriately adapting the exploration scheme, we show that one can derive regret guarantees that can be significantly more favorable than those previously known. Moreover, our analysis also modularizes the problematic quantities in achieving the conjectured minimax optimal rates in the most general setting of the problem.",
author = "Mehryar Mohri and Scott Yang",
year = "2016",
language = "English (US)",
series = "32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016",
publisher = "Association For Uncertainty in Artificial Intelligence (AUAI)",
pages = "815--824",
editor = "Dominik Janzing and Alexander Ihler",
booktitle = "32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016",
note = "32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016 ; Conference date: 25-06-2016 Through 29-06-2016",
}