Bayesian Optimization Augmented with Actively Elicited Expert Knowledge

Loading...
Thumbnail Image

URL

Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis

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, Samuel

Thesis advisor

Filstroff, Louis
Mikkola, Petrus

Keywords

Bayesian optimization, knowledge elicitation, active learning, preference learning, multi-task learning

Other note

Citation