TY  - GEN
T3  - Advances in Neural Information Processing Systems
TI  - Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
SN  - 1049-5258
PB  - NIPS
KW  - Artificial Intelligence
KW  -  Computer Science
KW  -  Computer Science
KW  -  Computer Science
KW  -  Information Systems
KW  -  Science & Technology
KW  -  Technology
AV  - public
ID  - discovery10198818
N2  - Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e.g., in wind energy systems. In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters). Standard algorithms assume no cost for switching their decisions at every round. However, in many practical applications, there is a cost associated with such changes, which should be minimized. We introduce the episodic CBO with movement costs problem and, based on the online learning approach for metrical task systems of Coester and Lee [19], propose a novel randomized mirror descent algorithm that makes use of Gaussian Process confidence bounds. We compare its performance with the offline optimal sequence for each episode and provide rigorous regret guarantees. We further demonstrate our approach on the important real-world application of altitude optimization for Airborne Wind Energy Systems. In the presence of substantial movement costs, our algorithm consistently outperforms standard CBO algorithms.
EP  - 13
UR  - https://openreview.net/forum?id=F-L7BxiE_V
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
A1  - Ramesh, Shyam Sundhar
A1  - Sessa, Pier Giuseppe
A1  - Krause, Andreas
A1  - Bogunovic, Ilija
Y1  - 2022/10/31/
ER  -