Publikacje

• Patryk Marszałek, Kamil Książek, Oleksii Furman, Ulvi Movsum-zada, Przemysław Spurek, Marek Śmieja, HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data, Neurocomputing,2026, https://www.sciencedirect.com/science/article/abs/pii/S0925231226001451

• Kamil Książek, Hubert Jastrzębski, Krzysztof Pniaczek, Bartosz Trojan, Michał Karp, Jacek Tabor, FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification, Knowledge-Based Systems ,2026, https://www.sciencedirect.com/science/article/abs/pii/S0950705126002224

• Piotr Borycki, Magdalena Trędowicz, Szymon Janusz, Jacek Tabor, Przemysław Spurek, Arkadiusz Lewicki, Łukasz Struski, EPIC: Explanation of Pretrained Image Classification Networks via Prototypes, AAAI ,2026, https://arxiv.org/pdf/2505.12897

• Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki, Bartłomiej Cupiał, Aneta Kaczyńska, Przemysław Spurek, Jacek Tabor, MeVGAN: GAN-based plugin model for video generation with applications in colonoscopy, PLOS One ,2025, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0312038.

• Wydmański, W.; Movsum-zada, U.; Tabor, J.; and Śmieja, M., VisTabNet: Adapting Vision Transformers for Tabular Data, SIAM Conference on Data Mining (SDM 2025), https://epubs.siam.org/doi/epdf/10.1137/1.9781611978520.54.

• Dominik Zimny, Artur Kasymov, Adam Kania, Jacek Tabor, Maciej Zięba, Marcin Mazur, Przemysław Spurek, MultiPlaneNeRF: Neural radiance field with non-trainable representation, Expert Systems With Applications, 2025, https://www.sciencedirect.com/science/article/abs/pii/S0957417425009728.

• Tadeusz Dziarmaga, Tomasz Arczewski, Marcin Mazur, Maciej Wołczyk,On-Policy Algorithms for Continual Reinforcement Learning (Student Abstract) , AAAI Conference on Artificial Intelligence, 2025, https://ojs.aaai.org/index.php/AAAI/article/view/35251.

• Kamil Książek, Wilhelm Masarczyk, Przemysław Głomb, Michał Romaszewski, Krisztián Buza, Przemysław Sekuła, Michał Cholewa, Katarzyna Kołodziej, Piotr Gorczyca, Magdalena Piegza, Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals, PLOS Computational Biology, 2025, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012983.

• Kamil Książek, Przemysław Spurek, HyperMask: Adaptive hypernetwork-based masks for continual learning, Neural Networks, 2025, https://www.sciencedirect.com/science/article/pii/S0893608025006161#d1e15331.

• Patryk Krukowski, Anna Bielawska, Kamil Książek, Paweł Wawrzyński, Paweł Batorski, Przemysław Spurek, HINT: Hypernetwork approach to training weight interval regions in continual learning , Information Sciences, 2025, https://www.sciencedirect.com/science/article/pii/S0020025525003937#ac0010.

• Przewięźlikowski, M.; Balestriero, R.; Jasiński, W.; Śmieja, M.; and Zieliński, B., Beyond [cls]: Exploring the true potential of Masked Image Modeling representations , International Conference on Computer Vision (ICCV), 2025.

• Kościukiewicz, J.; Zieliński, B.; and Rymarczyk, D, HCS-DFC: A Diffusion Classifier for Mode of Action Prediction Using Morphological Profiles, Conference on Computer Vision and Pattern Recognition Workshop on Computer Vision for Drug Discovery (CVDD CVPR), 2025.

• Marcin Sendera, Łukasz Struski, Kamil Książek, Kryspin Musiol, Jacek Tabor, Dawid Rymarczyk, SEMU: Singular Value Decomposition for Efficient Machine Unlearning, International Conference on Machine Learning (ICML), 2025, https://arxiv.org/abs/2502.07587.

• Łukasz Struski, Michał B. Bednarczyk, Igor T. Podolak, Jacek Tabor, LapSum – One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection, International Conference on Machine Learning (ICML), 2025, https://arxiv.org/abs/2503.06242.

• Magdalena Trędowicz, Marcin Mazur, Szymon Janusz, Arkadiusz Lewicki, Jacek Tabor, Łukasz Struski, PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification, European Conference on Artificial Intelligence (ECAI), 2025, https://arxiv.org/abs/2406.11443.

• Marcin Mazur, Tadeusz Dziarmaga, Piotr Kościelniak, Łukasz Struski, Tight Bounds for Jensen’s Gap with Applications to Variational Inference, the Conference on Information and Knowledge Management (CIKM), 2025, https://arxiv.org/abs/2502.03988.

• Osial, M.; Marczak, D.; and Zieliński, B., Parameter-Efficient Interventions for Enhanced Model Merging, SDM 2025.

• Pach, M.; Lewandowska, K.; Tabor, J.; Zieliński, B.; and Rymarczyk, D., LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision, ICLR 2025.

• Jajeśniak, D., Kościelniak, P., Zajdel, A., & Mazur, M., Deep dive into generative models through feature interpoint distances., Journal of Computational Science 2025, 102539, https://www.sciencedirect.com/science/article/abs/pii/S187775032500016X.

• Pardyl, A.; Kurzejamski, G.; Olszewski, J.; Trzciński, T.; and Zieliński, B., Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers, WACV 2025, https://arxiv.org/pdf/2309.13353.

• Olszewski, J.; Rymarczyk, D.; Wójcik, P.; Pach, M.; and Zieliński, B., TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration, WACV 2025, https://arxiv.org/pdf/2311.15335.

• Dariusz Jajeśniak, Piotr Kościelniak, Przemysław Klocek, Marcin Mazur, Interpoint Inception Distance: Gaussian-Free Evaluation of Deep Generative Models, ICCS 2024, https://www.iccs-meeting.org/archive/iccs2024/papers/148320279.pdf.

• Bedyhaj, A.; Tabor, J.; and Śmieja, M., StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024), https://link.springer.com/chapter/10.1007/978-981-97-2253-2_10.

• Suwała, A.; Wójcik, B.; Proszewska, M.; Tabor, J.; Spurek, P.; and Śmieja, M., Face Identity-Aware Disentanglement in StyleGAN., In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024), https://openaccess.thecvf.com/content/WACV2024/papers/Suwala_Face_Identity-Aware_Disentanglement_in_StyleGAN_WACV_2024_paper.pdf.

• Przemysław Spurek, Sebastian Winczowski, Maciej Ziȩba, Tomasz Trzciński, Kacper Kania, Marcin Mazur, Modeling 3D Surfaces with a Locally Conditioned Atlas, ICCS 2024, https://www.iccs-meeting.org/archive/iccs2024/papers/148330091.pdf.

• Magdalena Proszewska, Marcin Mazur, Tomasz Trzciński, Przemysław Spurek, HyperCube: Implicit Field Representations of Voxelized 3D Models (Student Abstract), AAAI 2024, https://ojs.aaai.org/index.php/AAAI/article/view/30499/32630.

• Mikołaj Sacha, Bartosz Jura, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations, AAAI 2024, https://arxiv.org/abs/2308.08162.

• Przewięźlikowski, M.; Osial, M.; Zieliński, B.; and Śmieja, M. , A deep cut into Split Federated Self-Supervised Learning, ECML PKDD 2024, https://link.springer.com/chapter/10.1007/978-3-031-70344-7_2.

• Michalski, T.; Rymarczyk, D.; Barczyk, D.; and Zieliński, B. , ProtoNCD: Prototypical Parts for Interpretable Novel Class Discovery, ESANN 2024, https://www.esann.org/sites/default/files/proceedings/2024/ES2024-70.pdf.

• Przewięźlikowski, M.; Pyla, M.; Zieliński, B.; Twardowski, B.; Tabor, J.; and Śmieja, M., Augmentation-aware Self-Supervised Learning with Conditioned Projector, Knowledge Based Systems (journal) 2024, https://linkinghub.elsevier.com/retrieve/pii/S0950705124012061.

• Łukasz Struski, Adam Pardyl, Jacek Tabor, Bartosz Zieliński, ProPML: Probability Partial Multi-label Learning, DSAA 2023, https://arxiv.org/abs/2403.07603.

• Rymarczyk, D.; van de Weijer, J.; Zieliński, B.; and Twardowski, B., ICICLE: Interpretable Class Incremental Continual Learning, ICCV 2023, https://ieeexplore.ieee.org/document/10378418.

• Łukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor, Bartosz Zieliński, ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging, ECAI 2023, https://arxiv.org/abs/2306.10535.

• Łukasz Struski, Marcin Mazur, Paweł Batorski, Przemysław Spurek, Jacek Tabor, Bounding Evidence and Estimating Log-Likelihood in VAE, AISTATS 2023, https://proceedings.mlr.press/v206/struski23a/struski23a.pdf.