%0 Conference Paper %1 SWLSz16:Conservative %A Shariff, R. %A Wu, Y. %A Lattimore, T. %A Szepesvári, Cs. %B ICML %D 2016 %K bandits, constrained finite-armed learning learning, online stochastic theory, %P 1254--1262 %T Conservative Bandits %X We study a novel multi-armed bandit problem that models the challenge faced by a company wishing to explore new strategies to maximize revenue whilst simultaneously maintaining their revenue above a fixed baseline, uniformly over time. While previous work addressed the problem under the weaker requirement of maintaining the revenue constraint only at a given fixed time in the future, the algorithms previously proposed are unsuitable due to their design under the more stringent constraints. We consider both the stochastic and the adversarial settings, where we propose, natural, yet novel strategies and analyze the price for maintaining the constraints. Amongst other things, we prove both high probability and expectation bounds on the regret, while we also consider both the problem of maintaining the constraints with high probability or expectation. For the adversarial setting the price of maintaining the constraint appears to be higher, at least for the algorithm considered. A lower bound is given showing that the algorithm for the stochastic setting is almost optimal. Empirical results obtained in synthetic environments complement our theoretical findings.

@inproceedings{SWLSz16:Conservative, abstract = { We study a novel multi-armed bandit problem that models the challenge faced by a company wishing to explore new strategies to maximize revenue whilst simultaneously maintaining their revenue above a fixed baseline, uniformly over time. While previous work addressed the problem under the weaker requirement of maintaining the revenue constraint only at a given fixed time in the future, the algorithms previously proposed are unsuitable due to their design under the more stringent constraints. We consider both the stochastic and the adversarial settings, where we propose, natural, yet novel strategies and analyze the price for maintaining the constraints. Amongst other things, we prove both high probability and expectation bounds on the regret, while we also consider both the problem of maintaining the constraints with high probability or expectation. For the adversarial setting the price of maintaining the constraint appears to be higher, at least for the algorithm considered. A lower bound is given showing that the algorithm for the stochastic setting is almost optimal. Empirical results obtained in synthetic environments complement our theoretical findings.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Shariff, R. and Wu, Y. and Lattimore, T. and {Sz}epesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/2b8fbf11a7a656b613474dfc8de7c087e/csaba}, booktitle = {ICML}, date-added = {2016-05-09 08:37:59 +0000}, date-modified = {2016-07-29 14:18:10 +0000}, interhash = {2ee9bb1a303c87f107e2ff35b224f623}, intrahash = {b8fbf11a7a656b613474dfc8de7c087e}, keywords = {bandits, constrained finite-armed learning learning, online stochastic theory,}, pages = {1254--1262}, pdf = {papers/ICML16-cbandit.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Conservative Bandits}, year = 2016 }