Skip to yearly menu bar Skip to main content


Poster

Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum

Shogo Iwazaki · Tomohiko Tanabe · Mitsuru Irie · Shion Takeno · Yu Inatsu

MR1 & MR2 - Number 175
[ ]
Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT

Abstract:

Real-world black-box optimization tasks often focus on obtaining the best reward, which includes an intrinsic random quantity from uncontrollable environmental factors. For this problem, we formulate a novel risk-seeking optimization problem whose aim is to obtain the best possible reward within a fixed budget under uncontrollable factors. We consider two settings: (1) environmental model setting for the case that we can observe uncontrollable environmental variables that affect the observation as the input of a target function, and (2) heteroscedastic model setting for the case that any uncontrollable variables cannot be observed. We propose a novel Bayesian optimization method called kernel explore-then-commit (kernel-ETC) and provide the regret upper bound for both settings. We demonstrate the effectiveness of kernel-ETC through several numerical experiments, including the hyperparameter tuning task and the simulation function derived from polymer synthesis real data.

Live content is unavailable. Log in and register to view live content