Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2022-06-13
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence (Macadamia)
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
49+10
Series
Abstract
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this thesis, we tackle the problem of incorporating expert knowledge into BO, with the goal of further accelerating the optimization, which has received very little attention so far. We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function. In particular, this allows for the expert knowledge to be transferred into the BO task. We introduce a specific architecture based on Siamese neural networks to handle the knowledge elicitation from pairwise queries. Experiments on various benchmark functions with both simulated and actual human experts show that the proposed method significantly speeds up BO even when the expert knowledge is biased compared to the objective function.Description
Supervisor
Kaski, SamuelThesis advisor
Filstroff, LouisMikkola, Petrus
Keywords
Bayesian optimization, knowledge elicitation, active learning, preference learning, multi-task learning
