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 -