Consistent Estimators for Learning to Defer to an Expert
Name
mozannar-mozannar-sm-idss-2023-thesis.pdf
Description
Thesis PDF
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1.17 MB
Format
Adobe PDF
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9aca68955e14a20ef7ec98678337ec7a
Author(s)
Mozannar, Hussein
Advisor(s)
Sontag, David
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however, this fact is largely ignored when designing these algorithms. In this thesis, we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert's decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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