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- W4361230931 abstract "Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utility of the best option (qEUBO) as a novel acquisition function for PBO. When the decision maker's responses are noise-free, we show that qEUBO is one-step Bayes optimal and thus equivalent to the popular knowledge gradient acquisition function. We also show that qEUBO enjoys an additive constant approximation guarantee to the one-step Bayes-optimal policy when the decision maker's responses are corrupted by noise. We provide an extensive evaluation of qEUBO and demonstrate that it outperforms the state-of-the-art acquisition functions for PBO across many settings. Finally, we show that, under sufficient regularity conditions, qEUBO's Bayesian simple regret converges to zero at a rate $o(1/n)$ as the number of queries, $n$, goes to infinity. In contrast, we show that simple regret under qEI, a popular acquisition function for standard BO often used for PBO, can fail to converge to zero. Enjoying superior performance, simple computation, and a grounded decision-theoretic justification, qEUBO is a promising acquisition function for PBO." @default.
- W4361230931 created "2023-03-31" @default.
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- W4361230931 date "2023-03-28" @default.
- W4361230931 modified "2023-10-18" @default.
- W4361230931 title "qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization" @default.
- W4361230931 doi "https://doi.org/10.48550/arxiv.2303.15746" @default.
- W4361230931 hasPublicationYear "2023" @default.
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