Bayesian Active Learning in the Presence of Nuisance Parameters

Published: 26 Apr 2024, Last Modified: 15 Jul 2024UAI 2024 oralEveryoneRevisionsBibTeXCC BY 4.0
Keywords: sequential optimal experimental design, Bayesian inference, active learning, prior misspecification
TL;DR: We analyze the effect of nuisance parameters on effective policies for Bayesian active learning.
Abstract: In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parameter, and no intrinsic interest in characterizing the entire data-generating process. Usually, the learner must also contend with additional sources of uncertainty or variables --- with nuisance parameters. Bayesian active learning, or sequential optimal experimental design, can straightforwardly accommodate the presence of nuisance parameters, and so is a natural active learning framework for such problems. However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference. We characterize the threat of negative interference and how it fundamentally changes the nature of the Bayesian active learner's task. We show that the extent of negative interference can be extremely large, and that accurate estimation of the nuisance parameters is critical to reducing it. The Bayesian active learner is confronted with a dilemma: whether to spend a finite acquisition budget in pursuit of estimation of the target or of the nuisance parameters. Our setting encompasses Bayesian transfer learning as a special case, and our results shed light on the phenomenon of negative transfer between learning environments.
Supplementary Material: zip
List Of Authors: Sloman, Sabina J and Bharti, Ayush and Martinelli, Julien and Kaski, Samuel
Latex Source Code: zip
Signed License Agreement: pdf
Submission Number: 545
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