Projective Preferential Bayesian Optimization

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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human-in-the-loop machine learning, gaussian process, preference learning, Bayesian optimization, Bayesian methods, machine learning, expert elicitation

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Mikkola, P, Todorovic, M, Järvi, J, Rinke, P & Kaski, S 2020, Projective Preferential Bayesian Optimization. in 37th International Conference on Machine Learning, ICML 2020. Proceedings of Machine Learning Research, International Machine Learning Society, pp. 6840-6848, International Conference on Machine Learning, Vienna, Austria, 12/07/2020. < http://proceedings.mlr.press/v119/mikkola20a.html >

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https://urn.fi/URN:NBN:fi:aalto-202106027165

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[article-cris] Perustieteiden korkeakoulu / SCI

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