@article{Dewaele_et_al_2026_Small_molecule_retrieval_what_are_we_optimizing_for,
  title   = {Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?}, 
  author  = {Gaetan De Waele and Marek Wydmuch and Krzysztof Dembczyński and Wojciech Kotłowski and Willem Waegeman},
  journal = {arXiv preprint arXiv:2602.16507},
  year    = {2026}
}

@article{Berkesewicz_et_al_2024_Classification_of_job_advertisements,
  author  = {Maciej Beręsewicz and Marek Wydmuch and Herman Cherniaiev and Robert Pater},
  title   = {Multilingual Hierarchical Classification of Job Advertisements for Job Vacancy Statistics},
  journal = {Journal of Official Statistics},
  volume  = {42},
  number  = {1},
  pages   = {23-61},
  year    = {2026},
  doi     = {10.1177/0282423X251395400},
  url     = {https://doi.org/10.1177/0282423X251395400},
}

@article{Borchmann_Wydmuch_2025_Query_and_Conquer_SQL,
  title   = {Query and Conquer: Execution-Guided SQL Generation}, 
  author  = {{\L}ukasz Borchmann and Marek Wydmuch},
  journal = {arXiv preprint arXiv:2503.24364},
  year    = {2025}
}

@article{Wydmuch_et_al_2024_LLMs_and_relational_databases,
  title   = {Tackling prediction tasks in relational databases with {LLM}s},
  author  = {Wydmuch, Marek and Borchmann, {\L}ukasz and Grali{\'n}ski, Filip},
  journal = {arXiv preprint arXiv:2411.11829},
  year    = {2024}
}

@inproceedings{Kotlowski_et_al_2024_OMMA,
  title     = {A General Online Algorithm for Optimizing Complex Performance Metrics},
  author    = {Kotlowski, Wojciech and Wydmuch, Marek and Schultheis, Erik and Babbar, Rohit and Dembczynski, Krzysztof},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  pages     = {25396--25425},
  year      = {2024},
  editor    = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume    = {235},
  series    = {Proceedings of Machine Learning Research},
  month     = {21--27 Jul},
  publisher = {PMLR},
  pdf       = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/kotlowski24a/kotlowski24a.pdf},
  url       = {https://proceedings.mlr.press/v235/kotlowski24a.html},
  abstract  = {We consider sequential maximization of performance metrics that are general functions of a confusion matrix of a classifier (such as precision, F-measure, or G-mean). Such metrics are, in general, non-decomposable over individual instances, making their optimization very challenging. While they have been extensively studied under different frameworks in the batch setting, their analysis in the online learning regime is very limited, with only a few distinguished exceptions. In this paper, we introduce and analyze a general online algorithm that can be used in a straightforward way with a variety of complex performance metrics in binary, multi-class, and multi-label classification problems. The algorithm’s update and prediction rules are appealingly simple and computationally efficient without the need to store any past data. We show the algorithm attains $\mathcal{O}(\frac{\ln n}{n})$ regret for concave and smooth metrics and verify the efficiency of the proposed algorithm in empirical studies.}
}

@inproceedings{Schultheis_et_al_2024_Consistent_algorithms,
  title     = {Consistent algorithms for multi-label classification with macro-at-$k$ metrics},
  author    = {Schultheis, Erik and Kotlowski, Wojciech and Wydmuch, Marek and Babbar, Rohit and Borman, Strom and Dembczynski, Krzysztof},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year      = {2024},
  url       = {https://openreview.net/forum?id=XOnya9gSdF}
}

@inproceedings{Schultheis_et_al_2023_Generalized_test_utilities,
  author    = {Schultheis, Erik and Wydmuch, Marek and Kotlowski, Wojciech and Babbar, Rohit and Dembczynski, Krzysztof},
  booktitle = {Advances in Neural Information Processing Systems},
  editor    = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
  pages     = {22269--22303},
  publisher = {Curran Associates, Inc.},
  title     = {Generalized test utilities for long-tail performance in extreme multi-label classification},
  url       = {https://proceedings.neurips.cc/paper_files/paper/2023/file/46994b3d6dd0fd5fca5f780af6259db5-Paper-Conference.pdf},
  volume    = {36},
  year      = {2023}
}

@inproceedings{Schultheis_et_al_2022_Missing_Labels_XMLC,
  author    = {Schultheis, Erik and Wydmuch, Marek and Babbar, Rohit and Dembczynski, Krzysztof},
  title     = {On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification},
  year      = {2022},
  isbn      = {9781450393850},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3534678.3539466},
  doi       = {10.1145/3534678.3539466},
  abstract  = {The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages     = {1547–1557},
  numpages  = {11},
  keywords  = {extreme classification, long-tail labels, missing labels, multi-label classification, propensity model, recommendation},
  location  = {Washington DC, USA},
  series    = {KDD '22}
}

@inproceedings{Wydmuch_et_al_2021_PSPLT,
  author    = {Wydmuch, Marek and Jasinska-Kobus, Kalina and Babbar, Rohit and Dembczynski, Krzysztof},
  title     = {Propensity-Scored Probabilistic Label Trees},
  year      = {2021},
  isbn      = {9781450380379},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3404835.3463084},
  doi       = {10.1145/3404835.3463084},
  booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages     = {2252–-2256},
  numpages  = {5},
  keywords  = {label trees, recommendation, multi-label classification, missing labels, tagging, propensity model, supervised learning, extreme classification, ranking},
  location  = {Virtual Event, Canada},
  series    = {SIGIR '21}
}

@inproceedings{Jasinska-Kobus_Wydmuch_et_al_2021_OPLT,
  title     = {Online probabilistic label trees},
  author    = {Jasinska-Kobus, Kalina and Wydmuch, Marek and Thiruvenkatachari, Devanathan and Dembczynski, Krzysztof},
  booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages     = {1801--1809},
  year      = {2021},
  editor    = {Banerjee, Arindam and Fukumizu, Kenji},
  volume    = {130},
  series    = {Proceedings of Machine Learning Research},
  month     = {13--15 Apr.},
  publisher = {PMLR},
  pdf       = {http://proceedings.mlr.press/v130/jasinska-kobus21a/jasinska-kobus21a.pdf},
  url       = {http://proceedings.mlr.press/v130/jasinska-kobus21a.html},
}

@article{Mortier_et_al_2021_Set_Valued_Prediction,
  author    = {Thomas Mortier and Marek Wydmuch and Eyke H{\"{u}}llermeier and Krzysztof Dembczynski and Willem Waegeman},
  title     = {Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification},
  journal   = {Data Mining and Knowledge Discovery},
  publisher = {Springer},
  year      = {2021},
  pages     = {1435-1469},
  numpages  = {35},
  url       = {https://doi.org/10.1007/s10618-021-00751-x},
  doi       = {10.1007/s10618-021-00751-x}
}

@article{Jasinska-Kobus_et_al_2020_PLT,
  title   = {Probabilistic Label Trees for Extreme Multi-label Classification},
  author  = {Jasinska-Kobus, Kalina and Wydmuch, Marek and Dembczynski, Krzysztof and Kuznetsov, Mikhail and Busa-Fekete, Robert},
  journal = {arXiv preprint arXiv:2009.11218},
  year    = {2020}
}

@incollection{Wydmuch_et_al_2018b_ExtremeText,
  title     = {A no-regret generalization of hierarchical softmax to extreme multi-label classification},
  author    = {Wydmuch, Marek and Jasinska, Kalina and Kuznetsov, Mikhail and Busa-Fekete, R\'{o}bert and Dembczynski, Krzysztof},
  booktitle = {Advances in Neural Information Processing Systems},
  volume    = {31},
  editor    = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
  pages     = {6358--6368},
  year      = {2018},
  publisher = {Curran Associates, Inc.},
  url       = {http://papers.nips.cc/paper/7872-a-no-regret-generalization-of-hierarchical-softmax-to-extreme-multi-label-classification.pdf}
}

@article{Wydmuch_et_al_2018a_ViZDoom_Competitions,
  author  = {Marek Wydmuch and Micha{\l} Kempka and Wojciech Ja\'skowski},
  title   = {{ViZDoom} {C}ompetitions: {P}laying {D}oom from {P}ixels},
  journal = {IEEE Transactions on Games},
  year    = {2019},
  volume  = {11},
  number  = {3},
  pages   = {248--259},
  doi     = {10.1109/TG.2018.2877047},
  ISSN    = {},
  month   = {Sept.},
  Note    = {The 2022 IEEE Transactions on Games Outstanding Paper Award}
}

@inproceedings{Kempka_et_al_2016_ViZDoom,
  author    = {Micha{\l} Kempka and Marek Wydmuch and Grzegorz Runc and Jakub Toczek and Wojciech Ja\'skowski},
  title     = {{ViZDoom}: A {D}oom-based {AI} Research Platform for Visual Reinforcement Learning},
  booktitle = {IEEE Conference on Computational Intelligence and Games},
  year      = {2016},
  url       = {https://arxiv.org/abs/1605.02097},
  address   = {Santorini, Greece},
  Month     = {Sept.},
  Pages     = {341--348},
  Publisher = {IEEE},
  Note      = {The Best Paper Award}
}