Hyperparameter Optimization via Interacting with Probabilistic Circuits

• URL: http://arxiv.org/abs/2505.17804v1
• Date: Fri, 23 May 2025 12:21:19 GMT
• Title: Hyperparameter Optimization via Interacting with Probabilistic Circuits
• Authors: Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting,
• Abstract summary: We develop a novel selection policy that enables an acquisition function-free generation of candidate points.<n>We demonstrate that our method achieves state-of-the-art performance in standard HPO and outperforms interactive BO baselines in interactive HPO.
• Score: 20.128718882698593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, to date, only a few allow to include human feedback. Existing interactive Bayesian optimization (BO) methods incorporate human beliefs by weighting the acquisition function with a user-defined prior distribution. However, in light of the non-trivial inner optimization of the acquisition function prevalent in BO, such weighting schemes do not always accurately reflect given user beliefs. We introduce a novel BO approach leveraging tractable probabilistic models named probabilistic circuits (PCs) as a surrogate model. PCs encode a tractable joint distribution over the hybrid hyperparameter space and evaluation scores. They enable exact conditional inference and sampling. Based on conditional sampling, we construct a novel selection policy that enables an acquisition function-free generation of candidate points (thereby eliminating the need for an additional inner-loop optimization) and ensures that user beliefs are reflected accurately in the selection policy. We provide a theoretical analysis and an extensive empirical evaluation, demonstrating that our method achieves state-of-the-art performance in standard HPO and outperforms interactive BO baselines in interactive HPO.

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