Szymon Jaroszewicz's publications

[1]: Krzysztof Rudaś, Szymon Jaroszewicz. Class flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data. arXiv, cs.LG, 2412.10009, 2024.
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[2]: Barbara Żogała-Siudem, Szymon Jaroszewicz. Variable screening for Lasso based on multidimensional indexing. Data Mining and Knowledge Discovery, 38(1), pages 49-78, 2024.
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[3]: Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch. Event prediction by estimating continuously the completion of a single temporal pattern's instances. Journal of Biomedical Informatics, 156, pages 104665, 2024.
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[4]: Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch. Early Multiple Temporal Patterns Based Event Prediction in Heterogeneous Multivariate Temporal Data. In Proceedings of the 2024 SIAM International Conference on Data Mining, SDM'24, pages 199-207, Houston, TX, USA, April, 2024.
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[5]: Krzysztof Rudaś, Szymon Jaroszewicz. Regularization for Uplift Regression. In Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD'23), pages 593-608, 2023.
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[6]: Szymon Jaroszewicz, Krzysztof Rudaś. Shrinkage Estimators for the Intercept in Linear and Uplift Regression. Scientific Annals of Computer Science, 33(1), pages 35-52, 2023.
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[7]: Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch. Continuous Prediction of a Time Intervals-Related Pattern's Completion. Knowledge and Information Systems, 65(11), pages 4797-4846, 2023.
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[8]: Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch. Continuously Predicting the Completion of a Time Intervals Related Pattern. In The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'23), pages 239-251, Osaka, Japan, May, 2023.
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[9]: Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, others (editors). Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Communications in Computer and Information Science, 1752, 2023, ISBN: 978-3-031-23617-4.
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[10]: B. Żogała-Siudem, S. Jaroszewicz. Fast stepwise regression based on multidimensional indexes. Information Sciences, 549, pages 288-309, 2021.
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[11]: R. M. Gubela, S. Lessmann, S. Jaroszewicz. Response transformation and profit decomposition for revenue uplift modeling. European Journal of Operational Research, 283(2), pages 647-661, 2020.
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[12]: Rudaś, Krzysztof, Jaroszewicz, Szymon. Shrinkage Estimators for Uplift Regression. In Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD'19), Würzburg, Germany, Sep, 2019.
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[13]: Rudaś, Krzysztof, Jaroszewicz, Szymon. Linear regression for uplift modeling. Data Mining and Knowledge Discovery, 32(5), pages 1275-1305, Sep, 2018.
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[14]: Oskar Jarczyk, Szymon Jaroszewicz, Adam Wierzbicki, Kamil Pawlak, Michal Jankowski-Lorek. Surgical teams on GitHub: Modeling performance of GitHub project development processes. Information and Software Technology, 100, pages 32-46, 2018.
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[15]: M. Sołtys, S. Jaroszewicz. Boosting algorithms for uplift modeling. CoRR, abs/1807.07909, 2018, arXiv preprint.
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[16]: Ł. Zaniewicz, S. Jaroszewicz. Lp-Support vector machines for uplift modeling. Knowledge and Information Systems, 53(1), pages 269-296, Oct, 2017.
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[17]: S. Jaroszewicz. Uplift Modeling. In Encyclopedia of Machine Learning and Data Mining, pages 1304-1309, Springer US, Boston, MA, 2017.
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[18]: M. Jankowski-Lorek, S. Jaroszewicz, Ł. Ostrowski, A. Wierzbicki. Verifying social network models of Wikipedia knowledge community. Information Sciences, 339, pages 158-174, 2016.
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[19]: S. Jaroszewicz, Ł. Zaniewicz. Székely Regularization for Uplift Modeling. In Challenges in Computational Statistics and Data Mining, pages 135-154, Springer International Publishing, Cham, 2016.
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[20]: M. Sołtys, S. Jaroszewicz, P. Rzepakowski. Ensemble methods for uplift modeling. Data Mining and Knowledge Discovery, 29(6), pages 1531-1559, Nov, 2015.
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[21]: Wyrwicz, L., Jaroszewicz, S., Rzepakowski, P., Bujko, K.. Uplift modeling in selection of patients to either radiotherapy or radiochemotherapy in resectable rectal cancer: reassessment of data from the phase 3 study. Annals of Oncology, 26(suppl_4), pages iv107, 2015, conference abstract.
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[22]: S. Jaroszewicz, P. Rzepakowski. Uplift modeling with survival data. In ACM SIGKDD Workshop on Health Informatics (HI-KDD'14), New York City, USA, August, 2014.
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[23]: M. Korzeń, Szymon Jaroszewicz. PaCAL: A Python Package for Arithmetic Computations with Random Variables. Journal of Statistical Software, 57(10), 5, 2014.
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[24]: O. Jarczyk, B. Gruszka, S. Jaroszewicz, L. Bukowski, A. Wierzbicki. GitHub Projects. Quality Analysis of Open-Source Software. In Proc. of the 6th International Conference on Social Informatics (SocInfo'14), pages 80-94, Barcelona, Spain, November, 2014, Best paper nominee.
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[25]: B. Żogała-Siudem, S. Jaroszewicz. Fast stepwise regression on Linked Data. In Proc. of the 1st Workshop on Linked Data for Knowledge Discovery (LD4KD) co-located with ECML/PKDD'14, pages 17-26, Nancy, France, September, 2014.
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[26]: Ł. Zaniewicz, S. Jaroszewicz. Support Vector Machines for Uplift Modeling. In The First IEEE ICDM Workshop on Causal Discovery (CD 2013), Dallas, Texas, December, 2013.
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[27]: L. Bukowski, M. Jankowski-Lorek, S. Jaroszewicz, M. Sydow. What Makes a Good Team of Wikipedia Editors?~A Preliminary Statistical Analysis. In Proc. of the 5th International Conference on Social Informatics (SocInfo'14), pages 14-28, Kyoto, Japan, November, 2013.
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[28]: M. Korzeń, S. Jaroszewicz, P. Klęsk. Logistic regression with weight grouping priors. Computational Statistics & Data Analysis, 64, pages 281-298, August, 2013.
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[29]: P. Rzepakowski, S. Jaroszewicz. Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, 32, pages 303-327, August, 2012.
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[30]: M. Jaśkowski, S. Jaroszewicz. Uplift Modeling for Clinical Trial Data. In ICML 2012 Workshop on Machine Learning for Clinical Data Analysis, Edinburgh, Scotland, June, 2012.
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[31]: P. Rzepakowski, S. Jaroszewicz. Uplift Modeling in Direct Marketing. Journal of Telecommunications and Information Technology, 2, pages 43-50, 2012.
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[32]: S. Jaroszewicz, M. Korzeń. Arithmetic Operations on Independent Random Variables: A Numerical Approach. SIAM Journal on Scientific Computing, 34, pages A1241-A1265, 2012.
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[33]: P. Rzepakowski, S. Jaroszewicz. Decision Trees for Uplift Modeling. In Proc. of the 10th International Conference on Data Mining (ICDM), pages 441-450, Sydney, Australia, December, 2010.
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[34]: S. Jaroszewicz. Using interesting sequences to interactively build Hidden Markov Models. Data Mining and Knowledge Discovery, 21(1), pages 186-220, 2010.
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[35]: S. Jaroszewicz. Discovering Interesting Patterns in Numerical Data with Background Knowledge. In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, pages 118-130, IGI Global, 2010.
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[36]: S. Jaroszewicz, T. Scheffer, D.A. Simovici. Scalable pattern mining with Bayesian networks as background knowledge. Data Mining and Knowledge Discovery, 18(1), pages 56-100, 2009.
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[37]: S. Jaroszewicz. Interactive HMM Construction Based on Interesting Sequences. In Proc. of Local Patterns to Global Models (LeGo'08) Workshop at the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'08), pages 82-91, Antwerp, Belgium, 2008.
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[38]: S. Jaroszewicz. Minimum Variance Associations -- Discovering Relationships in Numerical Data. In The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 172-183, Osaka, Japan, 2008.
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[39]: T. Calders, S. Jaroszewicz. Efficient AUC Optimization for Classification. In 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'07), pages 42-53, Warsaw, Poland, 2007, Best paper award.
(C) Springer Verlag (Lecture Notes in Computer Science 4702)
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[40]: S. Jaroszewicz, L. Ivantysynova, T. Scheffer. Schema Matching on Streams with Accuracy Guarantees. Intelligent Data Analysis, 12(3), pages 253-270, 2008.
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[41]: S. Jaroszewicz, M. Korzeń. Approximating Representations for Large Numerical Databases. In 7th SIAM International Conference on Data Mining (SDM'07), pages 521-526, Minneapolis, MN, 2007.
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[42]: S. Jaroszewicz, L. Ivantysynova, T. Scheffer. Accurate Schema Matching on Streams. In 4th International Workshop on Knowledge Discovery from Data Streams at the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), pages 3-12, 2006.
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[43]: T. Calders, B. Goethals, S. Jaroszewicz. Mining Rank-Correlated Sets of Numerical Attributes. In Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06), 2006.
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[44]: S. Jaroszewicz. Polynomial Association Rules with Applications to Logistic Regression. In Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06), 2006.
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[45]: S. Jaroszewicz, M. Korzeń. Comparison of Information Theoretical Measures for Reduct Finding. In Proc. of the 8th International Conference on Artificial Intelligence and Soft Computing (ICAISC'06), pages 518-527, Zakopane, June, 2006.
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[46]: D. A. Simovici, S. Jaroszewicz. A new metric splitting criterion for decision trees. Parallel Algorithms Appl., 21(4), pages 239-256, 2006.
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[47]: D. A. Simovici, S. Jaroszewicz. Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees. In 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD'06), pages 35-44, Singapore, 2006.
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[48]: S. Jaroszewicz, T. Scheffer. Fast Discovery of Unexpected Patterns in Data, Relative to a Bayesian Network. In 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005), pages 118-127, Chicago, IL, August, 2005.
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[49]: D. Simovici, S. Jaroszewicz. A New Metric Splitting Criterion for Decision Trees. International Journal of Parallel, Emergent and Distributed Systems, 21(4), pages 239-256, August, 2006.
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[50]: S. Jaroszewicz, W. Kosiński. Machine Learning for Speech Recognition Researchers. In Proceedings of the Speech Analysis, Synthesis and Recognition Applications of Phonetics Conference, Kraków, Poland, September, 2005, Publication on CD-ROM.
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[51]: M. Korzeń, S. Jaroszewicz. Finding Reducts Without Building the Discernibility Matrix.. In Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications (ISDA'05), pages 450-455, Wrocław, Poland, 2005.
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[52]: D. Simovici, S. Jaroszewicz. A Metric Approach to Building Decision Trees Based on Goodman-Kruskal Association Index. In PAKDD 2004, pages 181-190, Sydney, Australia, May, 2004.
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[53]: S. Jaroszewicz, D. A. Simovici, I. Rosenberg. Measures on Boolean Polynomials and Their Applications in Data Mining. Discrete Applied Mathematics, 144(1-2), pages 123-139, November, 2004.
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[54]: Szymon Jaroszewicz, Dan Simovici. Interestingness of Frequent Itemsets Using Bayesian Networks as Background Knowledge. In 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pages 178-186, Seattle, WA, August, 2004.
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[55]: S. Jaroszewicz, D. A. Simovici, W. P. Kuo, L. Ohno-Machado. The Goodman-Kruskal Ceofficient and its Applications in Genetic Diagnosis of Cancer. IEEE Transactions on Biomedical Engineering, 51(7), pages 1095-1102, July, 2004.
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[56]: Szymon Jaroszewicz. Information Theoretical and Combinatorial Methods in Data Mining. PhD thesis, University of Massachusetts Boston, December, 2003.
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[57]: Dan A. Simovici, Szymon Jaroszewicz. Generalized Conditional Entropy and Decision Trees. In Journees francophones d'Extraction et de Gestion de Connaissances (EGC 2003), pages 369-380, Lyon, France, January, 2003.
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[58]: S. Jaroszewicz, D. A. Simovici. Support Approximations Using Bonferroni-type Inequalities. In 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2002), pages 212-223, Helsinki, Finland, August, 2002.
(C) Springer Verlag (Lecture Notes in Computer Science 2431)
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[59]: S. Jaroszewicz, D. A. Simovici. Pruning Redundant Association Rules Using Maximum Entropy Principle. In Advances in Knowledge Discovery and Data Mining, 6th Pacific-Asia Conference, PAKDD'02, pages 135-147, Taipei, Taiwan, May, 2002.
(C) Springer Verlag (Lecture Notes in Computer Science 2336)
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[60]: I. Rosenberg, D. A. Simovici, S. Jaroszewicz. On Functions Defined on Free Boolean Algebras. In IEEE Intenrational Symposiun on Multiple-Valued Logic ISMVL'02, Boston, MA, May, 2002.
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[61]: S. Jaroszewicz, D. A. Simovici, I. Rosenberg. An Inclusion-Exclusion Result for Boolean Polynomials and Its Applications in Data Mining. In Workshop on Discrete Mathematics and Data Mining (DM&DM), Second SIAM International Conference on Data Mining, Arlington, VA, April, 2002.
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[62]: D. A. Simovici, S. Jaroszewicz. An Axiomatization of Partition Entropy. IEEE Transactions on Information Theory, 48(7), pages 2138-2142, July, 2002.
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[63]: S. Jaroszewicz, D. A. Simovici. A General Measure of Rule Interestingness. In 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2001), pages 253-265, 2001.
(C) Springer Verlag (Lecture Notes in Computer Science 2168)
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[64]: S. Jaroszewicz, D. A. Simovici. Data Mining of Weak Functional Decompositions. In Proc. of the 30th International Symposium on Multiple-Valued Logic (ISMVL 2000), pages 77-82, 2000.
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[65]: D. A. Simovici, S. Jaroszewicz. On Information-Theoretical Aspects of Relational Databases. In Finite versus Infinite, Springer Verlag, London, 2000.
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[66]: S. Jaroszewicz, D. A. Simovici. On Axiomatization of Conditional Entropy of Functions Between Finite Sets. In Proceedings of the 29th International Symposium on Multiple-Valued Logic, pages 24-28, Freiburg, Germany, 1999.
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[67]: S.Jaroszewicz. Minimization of Incompletely Specified Multiple-Valued Functions in Reed-Muller Domain. In Proc. of the 19th International Scientific Symposium for Students and Young Research Employees, pages 121-126, Zielona Góra, Poland, 1997, (in Polish).
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[68]: S.Jaroszewicz, V.Shmerko, S.Yanushkevich. Exact Irredundant Searching for a Minimal Reed-Muller Expansion for an Incompletely Specified MVL Function. In Proc. of the International Conference on Application of Computer Systems, pages 65-74, Szczecin, Poland, 1996.
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[69]: A.D.Zakrevskij, S.N.Yanushkevich, S.Jaroszewicz. Minimization of Reed-Muller Expansions for Systems of Incompletely Specified MVL Functions. In Proc. of the International Conference on Methods and Models in Automatics and Robotics (MMAR'96), pages 1085-1090, Międzyzdroje, Poland, 1996.
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