Robust Label Shift Quantification
In this paper, we investigate the label shift quantification problem. We propose robust estimators of the label distribution which turn out to coincide with the Maximum Likelihood Estimator. We analyze the theoretical aspects and derive deviation bounds for the proposed method, providing optimal guarantees in the well-specified case, along with notable robustness properties against outliers and contamination. Our results provide theoretical validation for empirical observations on the robustness of Maximum Likelihood Label Shift.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Binary Quantification and Dataset Shift: An Experimental Investigation
Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has …
Binary QuantificationQuantification under prior probability shift: the ratio estimator and its extensions
The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target popula…
Distribution Matching for Graph Quantification Under Structural Covariate Shift
Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to solve this task one should consider, both,…
A Benchmark for Text Quantification Learning Under Real-World Temporal Distribution Shift
Text quantification is a supervised learning task estimating the relative frequency of each class for a collection of uncategorized text documents. Quantification learning has an increasing number of applications in prac…
Sentiment AnalysisAdjusted Count Quantification Learning on Graphs
Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem ha…
Node Clustering