2 citations · 5 across the 8 of their papers we have counts for
9 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…
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…
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning
Paweł Teisseyre
Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in…
Feature ranking for multi-label classification using Markov Networks
Paweł Teisseyre
We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels,…
Linear regression model selection using p-values when the model dimension grows
Piotr Pokarowski, Jan Mielniczuk, Paweł Teisseyre
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied f…