入力位置の不確実性に対応するベイズ最適化
Bayesian optimisation under uncertain inputs
観測値だけでなく観測位置自体にも誤差がある状況で使えるUCB型ベイズ最適化アルゴリズムを提案し、理論解析と実データ実験で有効性を示した論文。
著者: Rafael Oliveira, Lionel Ott, Fabio Ramos
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In these cases, the estimation of the actual query location is also subject to uncertainty. In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain. The algorithm employs a Gaussian process model that takes probability distributions as inputs. Theoretical results are provided for both the proposed algorithm and a conventional UCB approach within the uncertain-inputs setting. Finally, we evaluate each method's performance experimentally, comparing them to other input noise aware BO approaches on simulated scenarios involving synthetic and real data.
