Model Selection in Logistic Regression Using p-Values and Greedy Search. - Jan Mielniczuk; Pawel Teisseyre (SIIS, 2011)

Using random subspace method for prediction and variable importance assessment in linear regression. - Jan Mielniczuk; Pawel Teisseyre (Comput. Stat. Data Anal., 2014)

Analysing Utterances in Polish Parliament to Predict Speaker's Background. - Piotr Przybyla; Pawel Teisseyre (J. Quant. Linguistics, 2014)

What Do Your Look-alikes Say about You? Exploiting Strong and Weak Similarities for Author Profiling. - Piotr Przybyla; Pawel Teisseyre (CLEF (Working Notes), 2015)

Asymptotic consistency and order specification for logistic classifier chains in multi-label learning. - Pawel Teisseyre (CoRR, 2016)

Random Subspace Method for high-dimensional regression with the R package regRSM. - Pawel Teisseyre; Robert A. Klopotek; Jan Mielniczuk (Comput. Stat., 2016)

Feature ranking for multi-label classification using Markov networks. - Pawel Teisseyre (Neurocomputing, 2016)

What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited. - Jan Mielniczuk; Pawel Teisseyre (Challenges in Computational Statistics and Data Mining, 2016)

CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization. - Pawel Teisseyre (Neurocomputing, 2017)

Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia. - Marcin Sydow; Katarzyna Baraniak; Pawel Teisseyre (J. Intell. Inf. Syst., 2017)

Information-Theoretic Feature Selection Using High-Order Interactions. - Mateusz Pawluk; Pawel Teisseyre; Jan Mielniczuk (LOD, 2018)

Stopping rules for mutual information-based feature selection. - Jan Mielniczuk; Pawel Teisseyre (Neurocomputing, 2019)

Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification. - Pawel Teisseyre; Damien Zufferey; Marta Slomka (Pattern Recognit., 2019)

Learning Classifier Chains Using Matrix Regularization: Application to Multimorbidity Prediction. - Pawel Teisseyre (ECAI, 2020)

Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique. - Pawel Teisseyre; Jan Mielniczuk; Michal J. Dabrowski (ICCS (3), 2020)

Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data. - Pawel Teisseyre; Jan Mielniczuk; Malgorzata Lazecka (ICCS (4), 2020)

Detection of Conditional Dependence Between Multiple Variables Using Multiinformation. - Jan Mielniczuk; Pawel Teisseyre (ICCS (6), 2021)

Controlling Costs in Feature Selection: Information Theoretic Approach. - Pawel Teisseyre; Tomasz Klonecki (ICCS (2), 2021)

Estimating the class prior for positive and unlabelled data via logistic regression. - Malgorzata Lazecka; Jan Mielniczuk; Pawel Teisseyre (Adv. Data Anal. Classif., 2021)

How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information. - Mariusz Kubkowski; Jan Mielniczuk; Pawel Teisseyre (J. Mach. Learn. Res., 2021)

Classifier chains for positive unlabelled multi-label learning. - Pawel Teisseyre (Knowl. Based Syst., 2021)

Joint Feature Selection and Classification for Positive Unlabelled Multi-Label Data Using Weighted Penalized Empirical Risk Minimization. - Pawel Teisseyre (Int. J. Appl. Math. Comput. Sci., 2022)

Joint estimation of posterior probability and propensity score function for positive and unlabelled data. - Konrad Furmanczyk; Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (CoRR, 2022)

Double Logistic Regression Approach to Biased Positive-Unlabeled Data. - Konrad Furmanczyk; Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (ECAI, 2023)

Cost-constrained Group Feature Selection Using Information Theory. - Tomasz Klonecki; Pawel Teisseyre; Jaesung Lee 001 (MDAI, 2023)

Effective Exploitation of Macroeconomic Indicators for Stock Direction Classification Using the Multimodal Fusion Transformer. - Tae-Won Lee; Pawel Teisseyre; Jaesung Lee 001 (IEEE Access, 2023)

Multilabel all-relevant feature selection using lower bounds of conditional mutual information. - Pawel Teisseyre; Jaesung Lee 001 (Expert Syst. Appl., 2023)

Cost-constrained feature selection in multilabel classification using an information-theoretic approach. - Tomasz Klonecki; Pawel Teisseyre; Jaesung Lee 001 (Pattern Recognit., 2023)

Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods. - Tomasz Klonecki; Pawel Teisseyre (Appl. Intell., 2023)

Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. - Pawel Teisseyre; Konrad Furmanczyk; Jan Mielniczuk (ECAI, 2024)

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features. - Wangduk Seo; Sung-Hyun Cho; Pawel Teisseyre; Jaesung Lee 001 (IEEE Access, 2024)

Cost-constrained multi-label group feature selection using shadow features. - Tomasz Klonecki; Pawel Teisseyre; Jaesung Lee 001 (CoRR, 2024)

Joint empirical risk minimization for instance-dependent positive-unlabeled data. - Wojciech Rejchel; Pawel Teisseyre; Jan Mielniczuk (Knowl. Based Syst., 2024)

Learning from biased positive-unlabeled data via threshold calibration. - Pawel Teisseyre; Timo Martens; Jessa Bekker; Jesse Davis (AISTATS, 2025)

Class prior estimation for positive-unlabeled learning when label shift occurs. - Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (CoRR, 2025)

A generalized approach to label shift: the Conditional Probability Shift Model. - Pawel Teisseyre; Jan Mielniczuk (CoRR, 2025)