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arXiv Machine Learning By Rikuto Matsumoto, Masanori Ishikura, Masayuki Karasuyama

A Unified Information-Theoretic Approach to Constrained Multi-Fidelity Multi-Objective Bayesian Optimization

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The paper introduces a unified information‑theoretic framework for Bayesian optimization that simultaneously handles multiple objectives, constraints, and fidelity levels. It defines query utility as the information gain about the highest‑fidelity feasible Pareto frontier and derives a tractable variational lower bound using truncated approximations. The resulting cost‑aware acquisition function leverages multi‑fidelity surrogate models, eliminating the need for separate heuristics, and is validated on synthetic, benchmark, and real‑world experiments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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