| 2026 | IDA | Detecting Propensity Score Shifts Across Groups in Positive-Unlabeled Data. | Illia Tesliuk, Pawel Teisseyre |
| 2025 | AISTATS | Learning from biased positive-unlabeled data via threshold calibration. | Pawel Teisseyre, Timo Martens, Jessa Bekker, Jesse Davis |
| 2025 | ECAI | A Generalized Approach to Label Shift: The Conditional Probability Shift Model. | Pawel Teisseyre, Jan Mielniczuk |
| 2024 | ECAI | Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. | Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk |
| 2023 | ECAI | Double Logistic Regression Approach to Biased Positive-Unlabeled Data. | Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre |
| 2023 | MDAI | Cost-constrained Group Feature Selection Using Information Theory. | Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee |
| 2021 | ICCS | Detection of Conditional Dependence Between Multiple Variables Using Multiinformation. | Jan Mielniczuk, Pawel Teisseyre |
| 2021 | ICCS | Controlling Costs in Feature Selection: Information Theoretic Approach. | Pawel Teisseyre, Tomasz Klonecki |
| 2020 | ECAI | Learning Classifier Chains Using Matrix Regularization: Application to Multimorbidity Prediction. | Pawel Teisseyre |
| 2020 | ICCS | Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique. | Pawel Teisseyre, Jan Mielniczuk, Michal J. Dabrowski |
| 2020 | ICCS | Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data. | Pawel Teisseyre, Jan Mielniczuk, Malgorzata Lazecka |