Jan Mielniczuk

According to our database, Jan Mielniczuk authored at least 32 papers between 1993 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Joint empirical risk minimization for instance-dependent positive-unlabeled data.

Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk

Knowl. Based Syst., 2024

Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning.

Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

Augmented Prediction of a True Class for Positive Unlabeled Data Under Selection Bias.

Jan Mielniczuk, Adam Wawrzenczyk

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios.

Jan Mielniczuk, Adam Wawrzenczyk

CoRR, 2023

Outlier Detection Under False Omission Rate Control.

Adam Wawrzenczyk, Jan Mielniczuk

Proceedings of the Computational Science - ICCS 2023, 2023

Enhancing naive classifier for positive unlabeled data based on logistic regression approach.

Mateusz Platek, Jan Mielniczuk

Proceedings of the 18th Conference on Computer Science and Intelligence Systems, 2023

One-Class Classification Approach to Variational Learning from Biased Positive Unlabeled Data.

Jan Mielniczuk, Adam Wawrzenczyk

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Double Logistic Regression Approach to Biased Positive-Unlabeled Data.

Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

2022

Information Theoretic Methods for Variable Selection - A Review.

Jan Mielniczuk

Entropy, 2022

Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods.

Jan Mielniczuk

Entropy, 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

Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data.

Adam Wawrzenczyk, Jan Mielniczuk

Int. J. Appl. Math. Comput. Sci., 2022

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

Estimating the class prior for positive and unlabelled data via logistic regression.

Malgorzata Lazecka, Jan Mielniczuk, Pawel Teisseyre

Adv. Data Anal. Classif., 2021

Multiple Testing of Conditional Independence Hypotheses Using Information-Theoretic Approach.

Malgorzata Lazecka, Jan Mielniczuk

Proceedings of the Modeling Decisions for Artificial Intelligence, 2021

Detection of Conditional Dependence Between Multiple Variables Using Multiinformation.

Jan Mielniczuk, Pawel Teisseyre

Proceedings of the Computational Science - ICCS 2021, 2021

2020

Analysis of Information-Based Nonparametric Variable Selection Criteria.

Malgorzata Lazecka, Jan Mielniczuk

Entropy, 2020

Selection Consistency of Lasso-Based Procedures for Misspecified High-Dimensional Binary Model and Random Regressors.

Mariusz Kubkowski, Jan Mielniczuk

Entropy, 2020

Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data.

Pawel Teisseyre, Jan Mielniczuk, Malgorzata Lazecka

Proceedings of the Computational Science - ICCS 2020, 2020

Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique.

Pawel Teisseyre, Jan Mielniczuk, Michal J. Dabrowski

Proceedings of the Computational Science - ICCS 2020, 2020

Distributions of a General Reduced-Order Dependence Measure and Conditional Independence Testing.

Mariusz Kubkowski, Malgorzata Lazecka, Jan Mielniczuk

Proceedings of the Computational Science - ICCS 2020, 2020

2019

Stopping rules for mutual information-based feature selection.

Jan Mielniczuk, Pawel Teisseyre

Neurocomputing, 2019

2018

Information-Theoretic Feature Selection Using High-Order Interactions.

Mateusz Pawluk, Pawel Teisseyre, Jan Mielniczuk

Proceedings of the Machine Learning, Optimization, and Data Science, 2018

2017

Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction.

Jan Mielniczuk, Marcin Rdzanowski

Entropy, 2017

2016

What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited.

Jan Mielniczuk, Pawel Teisseyre

Proceedings of the Challenges in Computational Statistics and Data Mining, 2016

Random Subspace Method for high-dimensional regression with the R package regRSM.

Pawel Teisseyre, Robert A. Klopotek, Jan Mielniczuk

Comput. Stat., 2016

2015

Combined l1 and greedy l0 penalized least squares for linear model selection.

Piotr Pokarowski, Jan Mielniczuk

J. Mach. Learn. Res., 2015

2014

Using random subspace method for prediction and variable importance assessment in linear regression.

Jan Mielniczuk, Pawel Teisseyre

Comput. Stat. Data Anal., 2014

2011

Model Selection in Logistic Regression Using p-Values and Greedy Search.

Jan Mielniczuk, Pawel Teisseyre

Proceedings of the Security and Intelligent Information Systems, 2011

2007

Decorrelation of Wavelet Coefficients for Long-Range Dependent Processes.

Jan Mielniczuk, Piotr Wojdyllo

IEEE Trans. Inf. Theory, 2007

Estimation of Hurst exponent revisited.

Jan Mielniczuk, Piotr Wojdyllo

Comput. Stat. Data Anal., 2007

1993

Consistency of multilayer perceptron regression estimators.

Jan Mielniczuk, Joanna Tyrcha

Neural Networks, 1993