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.
arXiv:2607. 00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small.
MF-SCBO is a new multi‑fidelity extension of Scalable Constrained Bayesian Optimization designed for high‑dimensional black‑box functions with black‑box constraints. It handles an arbitrary number of fidelity levels and non‑nested sampling, addressing gaps in existing methods. Experiments on standard benchmarks and challenging problems show that MF‑SCBO generally converges faster than both single‑fidelity SCBO and other multi‑fidelity approaches in high‑dimensional constrained settings.
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
arXiv:2509. 21725v3 Announce Type: replace Abstract: A bilevel optimization problem consists of two optimization problems nested as an upper- and a lower-level problem, in which the optimality of the lower-level problem defines a constraint for the upper-level problem.
arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.
arXiv:2602. 03901v5 Announce Type: replace Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization.
arXiv:2603. 07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality.
arXiv:2606. 18785v1 Announce Type: cross Abstract: Identifying Pareto optimal solutions is critical to support multi-objective decision-making.
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
The paper introduces an active preference learning framework for many-objective Bayesian optimization that models preferences as a Dirichlet-process mixture of latent archetypes. It uses mixture-aware information-theoretic query strategies to separately identify archetypes and refine preferences within each archetype, employing a hybrid acquisition policy. Experiments on synthetic benchmarks and a real-world chemical process design case study show that this approach outperforms existing preference-based Bayesian optimization methods and recovers interpretable latent preference structures.
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
arXiv:2406.20062v4 Announce Type: replace-cross Abstract: Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where g...