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MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching

Authors : Bastien Dussap, Gilles Blanchard, Badr-Eddine Chérief-Abdellatif

Published in: Machine Learning and Knowledge Discovery in Databases: Research Track

Publisher: Springer Nature Switzerland

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Abstract

Quantification learning deals with the task of estimating the target label distribution under label shift. In this paper, we first present a unifying framework, distribution feature matching (DFM), that recovers as particular instances various estimators introduced in previous literature. We derive a general performance bound for DFM procedures, improving in several key aspects upon previous bounds derived in particular cases. We then extend this analysis to study robustness of DFM procedures in the misspecified setting under departure from the exact label shift hypothesis, in particular in the case of contamination of the target by an unknown distribution. These theoretical findings are confirmed by a detailed numerical study on simulated and real-world datasets. We also introduce an efficient, scalable and robust version of kernel-based DFM using Random Fourier Features.

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previous chapter Overcoming the Limitations of Localization Uncertainty: Efficient and Exact Non-linear Post-processing and Calibration
next chapter Robust Classification of High-Dimensional Data Using Data-Adaptive Energy Distance
Footnotes
1
There can be large variability between samples coming from different laboratories, while there is homogeneity within each lab. The label shift hypothesis is therefore reasonable when keeping source and target from the same lab.
 
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Metadata
Title
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Authors
Bastien Dussap
Gilles Blanchard
Badr-Eddine Chérief-Abdellatif
Copyright Year
2023
Book
Machine Learning and Knowledge Discovery in Databases: Research Track

Print ISBN: 978-3-031-43423-5

Electronic ISBN: 978-3-031-43424-2

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-43424-2_5

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