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.
Original languageEnglish
Title of host publication37th International Conference on Machine Learning, ICML 2020
Pages6840-6848
Number of pages9
ISBN (Electronic)9781713821120
Publication statusPublished - 21 Nov 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Vienna, Austria
Duration: 12 Jul 202018 Jul 2020
Conference number: 37

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountryAustria
CityVienna
Period12/07/202018/07/2020

Keywords

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

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