Projective Preferential Bayesian Optimization

• URL: http://arxiv.org/abs/2002.03113v4
• Date: Fri, 14 Aug 2020 12:10:55 GMT
• Title: Projective Preferential Bayesian Optimization
• Authors: Petrus Mikkola, Milica Todorovi\'c, Jari J\"arvi, Patrick Rinke, Samuel Kaski
• Abstract summary: We propose a new type of Bayesian optimization for learning user preferences in high-dimensional spaces. We show that our framework is able to find a global minimum of a high-dimensional black-box function.
• Score: 12.431251769382888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian optimization is an effective method for finding extrema of a black-box function. We propose a new type of Bayesian optimization for learning user preferences in high-dimensional spaces. The central assumption is that the underlying objective function cannot be evaluated directly, but instead a minimizer along a projection can be queried, which we call a projective preferential query. The form of the query allows for feedback that is natural for a human to give, and which enables interaction. This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface. We demonstrate that our framework is able to find a global minimum of a high-dimensional black-box function, which is an infeasible task for existing preferential Bayesian optimization frameworks that are based on pairwise comparisons.

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