3 papers
stat.ML2026
Prior shift estimation for positive unlabeled data through the lens of kernel embedding
Jan Mielniczuk, Wojciech Rejchel, PaweÅ Teisseyre
We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assumed that the source data is partially o…
stat.ML2025
A generalized approach to label shift: the Conditional Probability Shift Model
PaweÅ Teisseyre, Jan Mielniczuk
In many practical applications of machine learning, a discrepancy often arises between a source distribution from which labeled training examples are drawn and a target distributio…
stat.ML2024
Cost-constrained multi-label group feature selection using shadow features
Tomasz Klonecki, PaweÅ Teisseyre, Jaesung Lee
We consider the problem of feature selection in multi-label classification, considering the costs assigned to groups of features. In this task, the goal is to select a subset of fe…