@inproceedings{a00b0d55a901460588db22508a595946,
title = "Principled Approaches for Learning to Defer with Multiple Experts",
abstract = "We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. We first introduce a new family of surrogate losses specifically tailored for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously. We then prove that these surrogate losses benefit from strong H-consistency bounds. We illustrate the application of our analysis through several examples of practical surrogate losses, for which we give explicit guarantees. These loss functions readily lead to the design of new learning to defer algorithms based on their minimization. While the main focus of this work is a theoretical analysis, we also report the results of several experiments on SVHN and CIFAR-10 datasets.",
keywords = "Consistency, Learning theory, Learning to defer",
author = "Anqi Mao and Mehryar Mohri and Yutao Zhong",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 18th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2024, and 22nd International Workshop on Combinatorial Image Analysis, IWCIA 2024 ; Conference date: 08-01-2024 Through 10-01-2024",
year = "2024",
doi = "10.1007/978-3-031-63735-3_7",
language = "English (US)",
isbn = "9783031637346",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "107--135",
editor = "Barneva, {Reneta P.} and Brimkov, {Valentin E.} and Brimkov, {Valentin E.} and Claudio Gentile and Aldo Pacchiano",
booktitle = "Artificial Intelligence and Image Analysis - 18th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2024, and 22nd International Workshop on Combinatorial Image Analysis, IWCIA 2024, Revised Selected Papers",
address = "Germany",
}