Jan Mielniczuk

43 papers A 5B 1C 1Journal 27Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	A Generalized Approach to Label Shift: The Conditional Probability Shift Model. ECAI Pawel Teisseyre, Jan Mielniczuk
2025	J	`jnl`	A generalized approach to label shift: the Conditional Probability Shift Model. CoRR Pawel Teisseyre, Jan Mielniczuk
2025	J	`jnl`	Accounting for label shift of positive unlabeled data under selection bias. Int. J. Appl. Math. Comput. Sci. Jan Mielniczuk, Adam Wawrzenczyk
2025	J	`jnl`	Class prior estimation for positive-unlabeled learning when label shift occurs. CoRR Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre
2025	J	`jnl`	Single-sample Versus Case-control Sampling Scheme for Positive Unlabeled Data: the Story of Two Scenarios. Fundam. Informaticae Jan Mielniczuk, Adam Wawrzenczyk
2024	A	`conf`	Augmented Prediction of a True Class for Positive Unlabeled Data Under Selection Bias. ECAI Jan Mielniczuk, Adam Wawrzenczyk
2024	J	`jnl`	Augmented prediction of a true class for Positive Unlabeled data under selection bias. CoRR Jan Mielniczuk, Adam Wawrzenczyk
2024	J	`jnl`	Joint empirical risk minimization for instance-dependent positive-unlabeled data. Knowl. Based Syst. Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk
2024	A	`conf`	Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. ECAI Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk
2024	J	`jnl`	Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. CoRR Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk
2023	A	`conf`	Double Logistic Regression Approach to Biased Positive-Unlabeled Data. ECAI Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre
2023	C	`conf`	Enhancing naive classifier for positive unlabeled data based on logistic regression approach. FedCSIS Mateusz Platek, Jan Mielniczuk
2023	J	`jnl`	Enhancing naive classifier for positive unlabeled data based on logistic regression approach. CoRR Mateusz Platek, Jan Mielniczuk
2023	J	`jnl`	Joint empirical risk minimization for instance-dependent positive-unlabeled data. CoRR Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk
2023	A	`conf`	One-Class Classification Approach to Variational Learning from Biased Positive Unlabeled Data. ECAI Jan Mielniczuk, Adam Wawrzenczyk
2023	—	`conf`	Outlier Detection Under False Omission Rate Control. ICCS (3) Adam Wawrzenczyk, Jan Mielniczuk
2023	J	`jnl`	Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios. CoRR Jan Mielniczuk, Adam Wawrzenczyk
2022	J	`jnl`	Information Theoretic Methods for Variable Selection - A Review. Entropy Jan Mielniczuk
2022	J	`jnl`	Joint estimation of posterior probability and propensity score function for positive and unlabelled data. CoRR Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre
2022	J	`jnl`	Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods. Entropy Jan Mielniczuk
2022	J	`jnl`	Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data. Int. J. Appl. Math. Comput. Sci. Adam Wawrzenczyk, Jan Mielniczuk
2021	—	`conf`	Detection of Conditional Dependence Between Multiple Variables Using Multiinformation. ICCS (6) Jan Mielniczuk, Pawel Teisseyre
2021	J	`jnl`	Estimating the class prior for positive and unlabelled data via logistic regression. Adv. Data Anal. Classif. Malgorzata Lazecka, Jan Mielniczuk, Pawel Teisseyre
2021	J	`jnl`	How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information. J. Mach. Learn. Res. Mariusz Kubkowski, Jan Mielniczuk, Pawel Teisseyre
2021	B	`conf`	Multiple Testing of Conditional Independence Hypotheses Using Information-Theoretic Approach. MDAI Malgorzata Lazecka, Jan Mielniczuk
2020	J	`jnl`	Analysis of Information-Based Nonparametric Variable Selection Criteria. Entropy Malgorzata Lazecka, Jan Mielniczuk
2020	—	`conf`	Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data. ICCS (4) Pawel Teisseyre, Jan Mielniczuk, Malgorzata Lazecka
2020	—	`conf`	Distributions of a General Reduced-Order Dependence Measure and Conditional Independence Testing. ICCS (7) Mariusz Kubkowski, Malgorzata Lazecka, Jan Mielniczuk
2020	J	`jnl`	Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors. Entropy Mariusz Kubkowski, Jan Mielniczuk
2020	—	`conf`	Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique. ICCS (3) Pawel Teisseyre, Jan Mielniczuk, Michal J. Dabrowski
2019	J	`jnl`	Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors. CoRR Mariusz Kubkowski, Jan Mielniczuk
2019	J	`jnl`	Stopping rules for mutual information-based feature selection. Neurocomputing Jan Mielniczuk, Pawel Teisseyre
2018	—	`conf`	Information-Theoretic Feature Selection Using High-Order Interactions. LOD Mateusz Pawluk, Pawel Teisseyre, Jan Mielniczuk
2017	J	`jnl`	Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction. Entropy Jan Mielniczuk, Marcin Rdzanowski
2016	—	`book`	Challenges in Computational Statistics and Data Mining Challenges in Computational Statistics and Data Mining Stan Matwin, Jan Mielniczuk
2016	J	`jnl`	Random Subspace Method for high-dimensional regression with the R package regRSM. Comput. Stat. Pawel Teisseyre, Robert A. Klopotek, Jan Mielniczuk
2016	—	`ch.`	What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited. Challenges in Computational Statistics and Data Mining Jan Mielniczuk, Pawel Teisseyre
2015	J	`jnl`	Combined l1 and greedy l0 penalized least squares for linear model selection. J. Mach. Learn. Res. Piotr Pokarowski, Jan Mielniczuk
2014	J	`jnl`	Using random subspace method for prediction and variable importance assessment in linear regression. Comput. Stat. Data Anal. Jan Mielniczuk, Pawel Teisseyre
2011	—	`conf`	Model Selection in Logistic Regression Using p-Values and Greedy Search. SIIS Jan Mielniczuk, Pawel Teisseyre
2007	J	`jnl`	Decorrelation of Wavelet Coefficients for Long-Range Dependent Processes. IEEE Trans. Inf. Theory Jan Mielniczuk, Piotr Wojdyllo
2007	J	`jnl`	Estimation of Hurst exponent revisited. Comput. Stat. Data Anal. Jan Mielniczuk, Piotr Wojdyllo
1993	J	`jnl`	Consistency of multilayer perceptron regression estimators. Neural Networks Jan Mielniczuk, Joanna Tyrcha