A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback
Preference feedback, in the form of pairwise comparisons rather than scalar scores, has seen increasing use in applications such as human-, laboratory-, and expert-in-the-loop design, as well as scientific discovery. We propose a Thompson Sampling (TS) approach to Bayesian optimization with preferential feedback that models comparisons using a monotone link on latent utility differences and leverages the dueling kernel induced by a base kernel. We provide a finite-time analysis showing that the performance of the proposed method matches that of standard TS for conventional Bayesian optimization with scalar feedback. The analysis exploits the anchor invariance of TS for challenger selectio...
| Authors | Joseph Lazzaro, Davide Buffelli, Da-shan Shiu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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