1 citations · 1 across the 2 of their papers we have counts for
4 papers
A generalized approach to label shift: the Conditional Probability Shift Model
Paweł 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…
Prior shift estimation for positive unlabeled data through the lens of kernel embedding
Jan Mielniczuk, Wojciech Rejchel, Paweł 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…
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning
Paweł Teisseyre, Konrad Furmańczyk, Jan Mielniczuk
The goal of positive-unlabeled (PU) learning is to train a binary classifier on the basis of training data containing positive and unlabeled instances, where unlabeled observations…
Joint empirical risk minimization for instance-dependent positive-unlabeled data
Wojciech Rejchel, Paweł Teisseyre, Jan Mielniczuk
Learning from positive and unlabeled data (PU learning) is actively researched machine learning task. The goal is to train a binary classification model based on a training dataset…