New Algorithms for Adaptive Prior Selection in Gaussian Process Bandits
Researchers have developed two new algorithms—Prior-Elimination GP-TS and HyperPrior GP-TS—that automatically select appropriate priors for Gaussian process bandits, addressing a gap where practitioners typically rely on methods without theoretical guarantees. Gaussian process bandits are used for black-box optimization of unknown functions, and the choice of prior significantly affects performance. The work provides theoretical regret bounds for one algorithm and demonstrates practical improvements over existing approaches on synthetic and real-world datasets.
This arXiv preprint introduces methods for jointly optimizing prior selection and regret minimization in Gaussian process (GP) bandits using Thompson sampling. The paper addresses a practical problem: while GP bandits are powerful tools for optimizing unknown functions, their performance depends heavily on the assumed prior distribution, which is rarely known in advance. Current practice relies on maximum likelihood estimation for hyperparameter selection, but this approach lacks theoretical justification. The authors propose two algorithms: Prior-Elimination GP-TS, which eliminates priors with poor predictive performance, and HyperPrior GP-TS, which uses a bi-level Thompson sampling scheme. They establish sublinear regret bounds for HP-GP-TS and validate both approaches through experiments on synthetic and real-world data, showing improvements over baseline methods.
What's missing
The paper does not discuss computational complexity or scalability of the proposed algorithms to high-dimensional problems, which would be relevant for practitioners considering implementation. Additionally, the specific real-world datasets used in experiments are not detailed in the abstract, limiting assessment of practical applicability across different domains.
What different sources said
- arXiv cs.LGCenter
Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
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