Consistency of multilayer perceptron regression estimators. - Jan Mielniczuk; Joanna Tyrcha (Neural Networks, 1993)

Estimation of Hurst exponent revisited. - Jan Mielniczuk; Piotr Wojdyllo (Comput. Stat. Data Anal., 2007)

Decorrelation of Wavelet Coefficients for Long-Range Dependent Processes. - Jan Mielniczuk; Piotr Wojdyllo (IEEE Trans. Inf. Theory, 2007)

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)

Combined l1 and greedy l0 penalized least squares for linear model selection. - Piotr Pokarowski; Jan Mielniczuk (J. Mach. Learn. Res., 2015)

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

Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction. - Jan Mielniczuk; Marcin Rdzanowski (Entropy, 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)

Distributions of a General Reduced-Order Dependence Measure and Conditional Independence Testing. - Mariusz Kubkowski; Malgorzata Lazecka; Jan Mielniczuk (ICCS (7), 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)

Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors. - Mariusz Kubkowski; Jan Mielniczuk (Entropy, 2020)

Analysis of Information-Based Nonparametric Variable Selection Criteria. - Malgorzata Lazecka; Jan Mielniczuk (Entropy, 2020)

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

Multiple Testing of Conditional Independence Hypotheses Using Information-Theoretic Approach. - Malgorzata Lazecka; Jan Mielniczuk (MDAI, 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)

Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data. - Adam Wawrzenczyk; Jan Mielniczuk (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)

Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods. - Jan Mielniczuk (Entropy, 2022)

Information Theoretic Methods for Variable Selection - A Review. - Jan Mielniczuk (Entropy, 2022)

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

One-Class Classification Approach to Variational Learning from Biased Positive Unlabeled Data. - Jan Mielniczuk; Adam Wawrzenczyk (ECAI, 2023)

Enhancing naive classifier for positive unlabeled data based on logistic regression approach. - Mateusz Platek; Jan Mielniczuk (FedCSIS, 2023)

Outlier Detection Under False Omission Rate Control. - Adam Wawrzenczyk; Jan Mielniczuk (ICCS (3), 2023)

Augmented Prediction of a True Class for Positive Unlabeled Data Under Selection Bias. - Jan Mielniczuk; Adam Wawrzenczyk (ECAI, 2024)

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

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

Single-sample Versus Case-control Sampling Scheme for Positive Unlabeled Data: the Story of Two Scenarios. - Jan Mielniczuk; Adam Wawrzenczyk (Fundam. Informaticae, 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)