Dependence detection in high-dimensional regression problems
What We Do

Research problems concern discovery of a minimal set of predictors influencing the target (its Markov Blanket) , prediction interaction between features and modeling of causal dependencies in uplift. Studies use mainly non-parametric approaches based on information-theoretic measures and adaptation of data-mining techniques to uplift. One of the goasl is to
construct stopping rules for greedy search of active variables.
construct stopping rules for greedy search of active variables.
Grants
Publications
Key works
Recent contributions
- J. Mielniczuk, P. Teisseyre (2019) Stopping rules for information-based feature selection, Neurocomputing, 255-274
- K. Rudaś, , S. Jaroszewicz (2018) Linear regression for uplift modeling. Data Mining and Knowledge Discovery, 1275-1305.
- J. Mielniczuk, P. Teisseyre (2018) A Deeper Look at Two Concepts of Measuring Gene-Gene Interactions: Logistic Regression and Interaction Information Revisited, Genetic Epidemiology
- M. Kubkowski, J. Mielniczuk (2018) Projections of a general binary model on a logistic regression, Linear Algebra and its Applications, 536, 152-173
- J. Mielniczuk, M. Rdzanowski (2017) Use of information measures and their approximations to detect predictive gene-gene interactions, Entropy, 19, 1-23
more...
more publications
Team

Jan Mielniczuk
Founder

Have a Project in Mind?
Please get in touch with the head of the group.