mlsanityData to 7 Oct 2026

PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization

arXiv 2204.05411 ↗First posted 11 Apr 2022cs.LG math.OCcited by 5

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Abstract (arXiv)

We present Parallel Feasible Pareto Frontier Entropy Search (PF2ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query. Due to the complexity of characterizing the mutual information between candidate evaluations and (feasible) Pareto frontiers, existing approaches must either employ crude approximations that significantly hamper their performance or rely on expensive inference schemes that substantially increase the optimization's computational overhead. By instead using a variational lower bound, PF2ES provides a low-cost and accurate estimate of the mutual information. We benchmark PF2ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 32 of its 78 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

32 of 78 references.

References · 32

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20 more
  1. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
  2. 2020Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraintscited by 81
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 3.

5 papers.

  1. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  2. 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spacescited by 13
  3. 2022A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designcited by 5
  4. 2025MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directionscited by 3
  5. 2025Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimizationcited by 1