2 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Cost-constrained multi-label group feature selection using shadow features
Tomasz Klonecki, Paweł 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…
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…
Double logistic regression approach to biased positive-unlabeled data
Konrad Furmańczyk, Jan Mielniczuk, Wojciech Rejchel +1
Positive and unlabelled learning is an important problem which arises naturally in many applications. The significant limitation of almost all existing methods lies in assuming tha…