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{PF}^2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization Under Unknown Constraints

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If you have a question about this talk, please contact James Allingham .

Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.

We present Parallel Feasible Pareto Frontier Entropy Search—- 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. Moreover, we are able to interpret our proposed acquisition function by exploring direct links with other popular multi-objective acquisition functions. We benchmark PF2ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.

This talk is part of the Machine Learning Reading Group @ CUED series.

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