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ORCIDPrzemyslaw Biecek
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- affiliation: Warsaw University of Technology, Poland
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2020 – today
- 2024
[j34]André Fonseca, Mikolaj Spytek, Przemyslaw Biecek, Clara Cordeiro
, Nuno Sepúlveda:
Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data. BioData Min. 17(1) (2024)- [j33]Hubert Baniecki, Dariusz Parzych, Przemyslaw Biecek:
The grammar of interactive explanatory model analysis. Data Min. Knowl. Discov. 38(5): 2596-2632 (2024) - [j32]Alistair Knott, Dino Pedreschi, Toshiya Jitsuzumi, Susan Leavy, David M. Eyers, Tapabrata Chakraborti, Andrew Trotman, Sundar Sundareswaran, Ricardo Baeza-Yates, Przemyslaw Biecek, Adrian Weller, Paul D. Teal, Subhadip Basu, Mehmet Haklidir, Virginia Morini, Stuart Russell, Yoshua Bengio:
AI content detection in the emerging information ecosystem: new obligations for media and tech companies. Ethics Inf. Technol. 26(4): 63 (2024) - [j31]Alistair Knott, Dino Pedreschi, Toshiya Jitsuzumi, Susan Leavy, David M. Eyers, Tapabrata Chakraborti, Andrew Trotman, Sundar Sundareswaran, Ricardo Baeza-Yates, Przemyslaw Biecek, Adrian Weller, Paul D. Teal, Subhadip Basu, Mehmet Haklidir, Virginia Morini, Stuart Russell, Yoshua Bengio:
Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies. Ethics Inf. Technol. 26(4): 71 (2024) - [j30]Hubert Baniecki, Przemyslaw Biecek:
Adversarial attacks and defenses in explainable artificial intelligence: A survey. Inf. Fusion 107: 102303 (2024) - [j29]Stanislaw Gizinski, Paulina Kaczynska, Hubert Ruczynski, Emilia Wisnios, Bartosz Pielinski, Przemyslaw Biecek, Julian Sienkiewicz:
Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation. J. Informetrics 18(4): 101572 (2024) - [j28]Weronika Hryniewska, Adrianna Grudzien, Przemyslaw Biecek:
LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanations. Mach. Learn. 113(5): 3143-3160 (2024) - [j27]Katarzyna Woznica, Mateusz Grzyb, Zuzanna Trafas, Przemyslaw Biecek:
Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks. Mach. Learn. 113(7): 4925-4949 (2024) - [c30]Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek:
Red-Teaming Segment Anything Model. CVPR Workshops 2024: 2947-2956 - [c29]Piotr Wilczynski, Wiktoria Mieleszczenko-Kowszewicz, Przemyslaw Biecek:
Resistance Against Manipulative AI: Key Factors and Possible Actions. ECAI 2024: 802-809 - [c28]Przemyslaw Biecek, Wojciech Samek:
Position: Explain to Question not to Justify. ICML 2024 - [c27]Paulina Tomaszewska, Przemyslaw Biecek:
Position: Do Not Explain Vision Models Without Context. ICML 2024 - [c26]Szymon Plotka, Maciej Chrabaszcz, Przemyslaw Biecek:
Swin SMT: Global Sequential Modeling for Enhancing 3D Medical Image Segmentation. MICCAI (8) 2024: 689-698 - [c25]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. ECML/PKDD (2) 2024: 125-142 - [c24]Avigyan Bhattacharya, Tapabrata Chakraborti, Subhadip Basu, Alistair Knott, Dino Pedreschi, Raja G. Chatila, Susan Leavy, David M. Eyers, Paul D. Teal, Przemyslaw Biecek:
Towards a crowdsourced framework for online hate speech moderation - a case study in the Indian political scenario. WebSci (Companion) 2024: 75-84 - [c23]Oleksandr Davydko, Vladimir Pavlov, Przemyslaw Biecek, Luca Longo:
SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps. xAI (4) 2024: 3-23 - [c22]Weronika Hryniewska-Guzik, Luca Longo, Przemyslaw Biecek:
CNN-Based Explanation Ensembling for Dataset, Representation and Explanations Evaluation. xAI (2) 2024: 346-368 - [e7]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I. Communications in Computer and Information Science 1947, Springer 2024, ISBN 978-3-031-50395-5 [contents] - [e6]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II. Communications in Computer and Information Science 1948, Springer 2024, ISBN 978-3-031-50484-6 [contents] - [i73]Paulina Tomaszewska, Elzbieta Sienkiewicz, Mai P. Hoang, Przemyslaw Biecek:
Deep spatial context: when attention-based models meet spatial regression. CoRR abs/2401.10044 (2024) - [i72]Weronika Hryniewska-Guzik, Bartosz Sawicki, Przemyslaw Biecek:
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques. CoRR abs/2401.17200 (2024) - [i71]Przemyslaw Bombinski, Patryk Szatkowski, Bartlomiej Sobieski, Tymoteusz Kwiecinski, Szymon Plotka, Mariusz Adamek, Marcin Banasiuk, Mariusz I. Furmanek, Przemyslaw Biecek:
Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography. CoRR abs/2402.11510 (2024) - [i70]Przemyslaw Biecek, Wojciech Samek:
Explain to Question not to Justify. CoRR abs/2402.13914 (2024) - [i69]Vladimir Zaigrajew, Hubert Baniecki, Lukasz Tulczyjew, Agata M. Wijata, Jakub Nalepa, Nicolas Longépé, Przemyslaw Biecek:
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI. CoRR abs/2403.08017 (2024) - [i68]Sophie Hanna Langbein, Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek, Marvin N. Wright:
Interpretable Machine Learning for Survival Analysis. CoRR abs/2403.10250 (2024) - [i67]Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek:
Red-Teaming Segment Anything Model. CoRR abs/2404.02067 (2024) - [i66]Weronika Hryniewska-Guzik, Jakub Bilski, Bartosz Chrostowski, Jakub Drak Sbahi, Przemyslaw Biecek:
A comparative analysis of deep learning models for lung segmentation on X-ray images. CoRR abs/2404.06455 (2024) - [i65]Weronika Hryniewska-Guzik, Luca Longo, Przemyslaw Biecek:
CNN-based explanation ensembling for dataset, representation and explanations evaluation. CoRR abs/2404.10387 (2024) - [i64]Bartlomiej Sobieski, Przemyslaw Biecek:
Global Counterfactual Directions. CoRR abs/2404.12488 (2024) - [i63]Piotr Wilczynski, Wiktoria Mieleszczenko-Kowszewicz, Przemyslaw Biecek:
Resistance Against Manipulative AI: key factors and possible actions. CoRR abs/2404.14230 (2024) - [i62]Paulina Tomaszewska, Przemyslaw Biecek:
Position paper: Do not explain (vision models) without context. CoRR abs/2404.18316 (2024) - [i61]Mustafa Cavus, Przemyslaw Biecek:
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification. CoRR abs/2405.01557 (2024) - [i60]Paulina Tomaszewska, Mateusz Sperkowski, Przemyslaw Biecek:
Does context matter in digital pathology? CoRR abs/2405.14301 (2024) - [i59]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. CoRR abs/2406.09069 (2024) - [i58]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
Efficient and Accurate Explanation Estimation with Distribution Compression. CoRR abs/2406.18334 (2024) - [i57]Szymon Plotka, Maciej Chrabaszcz, Przemyslaw Biecek:
Swin SMT: Global Sequential Modeling in 3D Medical Image Segmentation. CoRR abs/2407.07514 (2024) - [i56]Maciej Chrabaszcz, Hubert Baniecki, Piotr Komorowski, Szymon Plotka, Przemyslaw Biecek:
Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models. CoRR abs/2407.16653 (2024) - 2023
- [j26]Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek:
survex: an R package for explaining machine learning survival models. Bioinform. 39(12) (2023) - [j25]Alistair Knott, Dino Pedreschi, Raja Chatila, Tapabrata Chakraborti, Susan Leavy, Ricardo Baeza-Yates, David M. Eyers, Andrew Trotman, Paul D. Teal, Przemyslaw Biecek, Stuart Russell, Yoshua Bengio:
Generative AI models should include detection mechanisms as a condition for public release. Ethics Inf. Technol. 25(4): 55 (2023) - [j24]Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek:
SurvSHAP(t): Time-dependent explanations of machine learning survival models. Knowl. Based Syst. 262: 110234 (2023) - [c21]Hubert Baniecki, Bartlomiej Sobieski, Przemyslaw Bombinski, Patryk Szatkowski, Przemyslaw Biecek:
Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics. AIME 2023: 65-74 - [c20]Piotr Komorowski, Hubert Baniecki, Przemyslaw Biecek:
Towards Evaluating Explanations of Vision Transformers for Medical Imaging. CVPR Workshops 2023: 3726-3732 - [c19]Adrian Stando, Mustafa Cavus, Przemyslaw Biecek:
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems. LIDTA 2023: 16-30 - [e5]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
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, Springer 2023, ISBN 978-3-031-23617-4 [contents] - [e4]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
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 II. Communications in Computer and Information Science 1753, Springer 2023, ISBN 978-3-031-23632-7 [contents] - [i55]Piotr Wilczynski, Artur Zólkowski, Mateusz Krzyzinski, Emilia Wisnios, Bartosz Pielinski, Stanislaw Gizinski, Julian Sienkiewicz, Przemyslaw Biecek:
HADES: Homologous Automated Document Exploration and Summarization. CoRR abs/2302.13099 (2023) - [i54]Przemyslaw Biecek, Hubert Baniecki, Mateusz Krzyzinski:
Performance is not enough: a story of the Rashomon's quartet. CoRR abs/2302.13356 (2023) - [i53]Mikolaj Spytek, Weronika Hryniewska-Guzik, Jaroslaw Zygierewicz, Jacek Rogala, Przemyslaw Biecek:
Challenges facing the explainability of age prediction models: case study for two modalities. CoRR abs/2303.06640 (2023) - [i52]Hubert Baniecki, Bartlomiej Sobieski, Przemyslaw Bombinski, Patryk Szatkowski, Przemyslaw Biecek:
Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics. CoRR abs/2303.09817 (2023) - [i51]Piotr Komorowski, Hubert Baniecki, Przemyslaw Biecek:
Towards Evaluating Explanations of Vision Transformers for Medical Imaging. CoRR abs/2304.06133 (2023) - [i50]Weronika Hryniewska, Piotr Czarnecki, Jakub Wisniewski, Przemyslaw Bombinski, Przemyslaw Biecek:
Prevention is better than cure: a case study of the abnormalities detection in the chest. CoRR abs/2305.10961 (2023) - [i49]Hubert Baniecki, Przemyslaw Biecek:
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey. CoRR abs/2306.06123 (2023) - [i48]Katarzyna Woznica, Piotr Wilczynski, Przemyslaw Biecek:
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets. CoRR abs/2306.11636 (2023) - [i47]Adrian Stando, Mustafa Cavus, Przemyslaw Biecek:
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems. CoRR abs/2307.00157 (2023) - [i46]Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti, Andreas Holzinger, Przemyslaw Biecek:
Explainable AI with counterfactual paths. CoRR abs/2307.07764 (2023) - [i45]Weronika Hryniewska-Guzik, Maria Kedzierska, Przemyslaw Biecek:
Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans. CoRR abs/2308.01137 (2023) - [i44]Katarzyna Kobylinska, Mateusz Krzyzinski, Rafal Machowicz, Mariusz Adamek, Przemyslaw Biecek:
Exploration of Rashomon Set Assists Explanations for Medical Data. CoRR abs/2308.11446 (2023) - [i43]Mustafa Cavus, Adrian Stando, Przemyslaw Biecek:
Glocal Explanations of Expected Goal Models in Soccer. CoRR abs/2308.15559 (2023) - [i42]Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek:
survex: an R package for explaining machine learning survival models. CoRR abs/2308.16113 (2023) - [i41]Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger, Bastian Pfeifer, Anna Saranti, Przemyslaw Biecek:
Be Careful When Evaluating Explanations Regarding Ground Truth. CoRR abs/2311.04813 (2023) - [i40]Stanislaw Gizinski, Paulina Kaczynska, Hubert Ruczynski, Emilia Wisnios, Bartosz Pielinski, Przemyslaw Biecek, Julian Sienkiewicz:
Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation. CoRR abs/2312.12881 (2023) - 2022
- [j23]Angela Lombardi, Domenico Diacono, Nicola Amoroso, Przemyslaw Biecek, Alfonso Monaco, Loredana Bellantuono, Ester Pantaleo, Giancarlo Logroscino, Roberto De Blasi, Sabina Tangaro, Roberto Bellotti:
A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease. Brain Informatics 9(1): 17 (2022) - [j22]Michael Bücker, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek:
Transparency, auditability, and explainability of machine learning models in credit scoring. J. Oper. Res. Soc. 73(1): 70-90 (2022) - [j21]Alicja Gosiewska, Katarzyna Woznica, Przemyslaw Biecek:
Interpretable meta-score for model performance. Nat. Mac. Intell. 4(9): 792-800 (2022) - [j20]Jakub Wisniewski, Przemyslaw Biecek:
fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models. R J. 14(1): 227-243 (2022) - [c18]Hubert Baniecki, Przemyslaw Biecek:
Manipulating SHAP via Adversarial Data Perturbations (Student Abstract). AAAI 2022: 12907-12908 - [c17]Mustafa Cavus, Przemyslaw Biecek:
Explainable expected goal models for performance analysis in football analytics. DSAA 2022: 1-9 - [c16]Alicja Gosiewska, Katarzyna Woznica, Przemyslaw Biecek:
Interpretable Meta-Score for Model Performance: Extended Abstract. Meta-Knowledge Transfer @ ECML/PKDD 2022: 75-77 - [c15]Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek:
Fooling Partial Dependence via Data Poisoning. ECML/PKDD (3) 2022: 121-136 - [i39]Katarzyna Woznica, Mateusz Grzyb, Zuzanna Trafas, Przemyslaw Biecek:
Consolidated learning - a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV. CoRR abs/2201.11815 (2022) - [i38]Mustafa Cavus, Przemyslaw Biecek:
Explainable expected goal models for performance analysis in football analytics. CoRR abs/2206.07212 (2022) - [i37]Przemyslaw Biecek:
Performance, Opaqueness, Consequences, and Assumptions: Simple questions for responsible planning of machine learning solutions. CoRR abs/2208.09966 (2022) - [i36]Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek:
SurvSHAP(t): Time-dependent explanations of machine learning survival models. CoRR abs/2208.11080 (2022) - [i35]Artur Zólkowski, Mateusz Krzyzinski, Piotr Wilczynski, Stanislaw Gizinski, Emilia Wisnios, Bartosz Pielinski, Julian Sienkiewicz, Przemyslaw Biecek:
Climate Policy Tracker: Pipeline for automated analysis of public climate policies. CoRR abs/2211.05852 (2022) - 2021
- [j19]Alicja Gosiewska, Anna Kozak, Przemyslaw Biecek:
Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering. Decis. Support Syst. 150: 113556 (2021) - [j18]Adam Gabriel Dobrakowski, Agnieszka Mykowiecka, Malgorzata Marciniak, Wojciech Jaworski, Przemyslaw Biecek:
Interpretable segmentation of medical free-text records based on word embeddings. J. Intell. Inf. Syst. 57(3): 447-465 (2021) - [j17]Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek:
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python. J. Mach. Learn. Res. 22: 214:1-214:7 (2021) - [j16]Weronika Hryniewska, Przemyslaw Bombinski, Patryk Szatkowski, Paulina Tomaszewska, Artur Przelaskowski, Przemyslaw Biecek:
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies. Pattern Recognit. 118: 108035 (2021) - [c14]Hubert Baniecki, Przemyslaw Biecek:
Responsible Prediction Making of COVID-19 Mortality (Student Abstract). AAAI 2021: 15755-15756 - [c13]Tomasz Stanislawek, Filip Gralinski, Anna Wróblewska, Dawid Lipinski, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemyslaw Biecek:
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts. ICDAR (1) 2021: 564-579 - [c12]Barbara Rychalska, Piotr Babel, Konrad Goluchowski, Andrzej Michalowski, Jacek Dabrowski, Przemyslaw Biecek:
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. ICONIP (4) 2021: 338-352 - [c11]Wojciech Jaworski, Przemyslaw Biecek, Adam Gabriel Dobrakowski, Malgorzata Marciniak, Agnieszka Mykowiecka, Agnieszka Morusiewicz, Joanna Przetacka, Lukasz Kaminski:
Supporting Doctor's Decisions Based on Electronic Medical Documentation in Polish. MedInfo 2021: 1076-1077 - [c10]Katarzyna Woznica, Przemyslaw Biecek:
Towards Explainable Meta-learning. PKDD/ECML Workshops (1) 2021: 505-520 - [e3]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e2]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i34]Jakub Wisniewski, Przemyslaw Biecek:
fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation. CoRR abs/2104.00507 (2021) - [i33]Katarzyna Pekala, Katarzyna Woznica, Przemyslaw Biecek:
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure. CoRR abs/2104.03403 (2021) - [i32]Przemyslaw Biecek, Marcin Chlebus, Janusz Gajda, Alicja Gosiewska, Anna Kozak, Dominik Ogonowski, Jakub Sztachelski, Piotr Wojewnik:
Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models. CoRR abs/2104.06735 (2021) - [i31]Tomasz Stanislawek, Filip Gralinski, Anna Wróblewska, Dawid Lipinski, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemyslaw Biecek:
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts. CoRR abs/2105.05796 (2021) - [i30]Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek:
Fooling Partial Dependence via Data Poisoning. CoRR abs/2105.12837 (2021) - [i29]Katarzyna Woznica, Katarzyna Pekala, Hubert Baniecki, Wojciech Kretowicz, Elzbieta Sienkiewicz, Przemyslaw Biecek:
Do not explain without context: addressing the blind spot of model explanations. CoRR abs/2105.13787 (2021) - [i28]Stanislaw Gizinski, Michal Kuzba, Bartosz Pielinski, Julian Sienkiewicz, Stanislaw Laniewski, Przemyslaw Biecek:
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence. CoRR abs/2108.06216 (2021) - [i27]Weronika Hryniewska, Adrianna Grudzien, Przemyslaw Biecek:
LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations. CoRR abs/2111.08094 (2021) - 2020
- [j15]Rafal Cylwa, Kornel Kielczewski, Marta Machnik, Urszula Oleksiewicz, Przemyslaw Biecek:
KRAB ZNF explorer - the online tool for the exploration of the transcriptomic profiles of KRAB-ZNF factors in The Cancer Genome Atlas. Bioinform. 36(3): 980-981 (2020) - [c9]Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek:
Explainable AI Methods - A Brief Overview. xxAI@ICML 2020: 13-38 - [c8]Adam Gabriel Dobrakowski, Agnieszka Mykowiecka, Malgorzata Marciniak, Wojciech Jaworski, Przemyslaw Biecek:
Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings. ISMIS 2020: 45-55 - [c7]Michal Kuzba, Przemyslaw Biecek:
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations. PKDD/ECML Workshops 2020: 447-459 - [e1]Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla:
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [i26]Alicja Gosiewska, Przemyslaw Biecek:
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering. CoRR abs/2002.04267 (2020) - [i25]Katarzyna Woznica, Przemyslaw Biecek:
Towards better understanding of meta-features contributions. CoRR abs/2002.04276 (2020) - [i24]Michal Kuzba, Przemyslaw Biecek:
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations. CoRR abs/2002.05674 (2020) - [i23]Filip Gralinski, Tomasz Stanislawek, Anna Wróblewska, Dawid Lipinski, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemyslaw Biecek:
Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout. CoRR abs/2003.02356 (2020) - [i22]Hubert Baniecki, Przemyslaw Biecek:
The Grammar of Interactive Explanatory Model Analysis. CoRR abs/2005.00497 (2020) - [i21]Alicja Gosiewska, Katarzyna Woznica, Przemyslaw Biecek:
Interpretable Meta-Measure for Model Performance. CoRR abs/2006.02293 (2020) - [i20]Katarzyna Woznica, Przemyslaw Biecek:
Does imputation matter? Benchmark for predictive models. CoRR abs/2007.02837 (2020) - [i19]Wojciech Kretowicz, Przemyslaw Biecek:
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets. CoRR abs/2008.13162 (2020) - [i18]Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek:
Landscape of R packages for eXplainable Artificial Intelligence. CoRR abs/2009.13248 (2020) - [i17]Michael Bücker, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek:
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring. CoRR abs/2009.13384 (2020) - [i16]Weronika Hryniewska, Przemyslaw Bombinski, Patryk Szatkowski, Paulina Tomaszewska, Artur Przelaskowski, Przemyslaw Biecek:
Do not repeat these mistakes - a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection. CoRR abs/2012.08333 (2020) - [i15]Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek:
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python. CoRR abs/2012.14406 (2020)
2010 – 2019
- 2019
- [j14]Michal Kuzba, Ewa Baranowska, Przemyslaw Biecek:
pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python. J. Open Source Softw. 4(37): 1389 (2019) - [j13]Kamil Romaszko, Magda Tatarynowicz, Mateusz Urbanski, Przemyslaw Biecek:
modelDown: automated website generator with interpretable documentation for predictive machine learning models. J. Open Source Softw. 4(38): 1444 (2019) - [j12]Hubert Baniecki, Przemyslaw Biecek:
modelStudio: Interactive Studio with Explanations for ML Predictive Models. J. Open Source Softw. 4(43): 1798 (2019) - [j11]Alicja Gosiewska, Przemyslaw Biecek:
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics. R J. 11(2): 85 (2019) - [j10]Mateusz Staniak, Przemyslaw Biecek:
The Landscape of R Packages for Automated Exploratory Data Analysis. R J. 11(2): 347 (2019) - [c6]Katarzyna Kobylinska, Tomasz Mikolajczyk, Mariusz Adamek, Tadeusz Orlowski, Przemyslaw Biecek:
Explainable Machine Learning for Modeling of Early Postoperative Mortality in Lung Cancer. KR4HC/ProHealth/TEAAM@AIME 2019: 161-174 - [c5]Tomasz Stanislawek, Anna Wróblewska, Alicja Wójcicka, Daniel Ziembicki, Przemyslaw Biecek:
Named Entity Recognition - Is There a Glass Ceiling? CoNLL 2019: 624-633 - [c4]Barbara Rychalska, Dominika Basaj, Alicja Gosiewska, Przemyslaw Biecek:
Models in the Wild: On Corruption Robustness of Neural NLP Systems. ICONIP (3) 2019: 235-247 - [i14]Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, Przemyslaw Biecek:
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning. CoRR abs/1902.11035 (2019) - [i13]Alicja Gosiewska, Przemyslaw Biecek:
iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models. CoRR abs/1903.11420 (2019) - [i12]Mateusz Staniak, Przemyslaw Biecek:
The Landscape of R Packages for Automated Exploratory Data Analysis. CoRR abs/1904.02101 (2019) - [i11]Adam Gabriel Dobrakowski, Agnieszka Mykowiecka, Malgorzata Marciniak, Wojciech Jaworski, Przemyslaw Biecek:
Clustering of Medical Free-Text Records Based on Word Embeddings. CoRR abs/1907.04152 (2019) - [i10]Przemyslaw Biecek:
Model Development Process. CoRR abs/1907.04461 (2019) - [i9]Alicja Gosiewska, Mateusz Bakala, Katarzyna Woznica, Maciej Zwolinski, Przemyslaw Biecek:
EPP: interpretable score of model predictive power. CoRR abs/1908.09213 (2019) - [i8]Tomasz Stanislawek, Anna Wróblewska, Alicja Wójcicka, Daniel Ziembicki, Przemyslaw Biecek:
Named Entity Recognition - Is there a glass ceiling? CoRR abs/1910.02403 (2019) - 2018
- [j9]Przemyslaw Biecek:
DALEX: Explainers for Complex Predictive Models in R. J. Mach. Learn. Res. 19: 84:1-84:5 (2018) - [j8]Aleksandra Grudziaz, Alicja Gosiewska, Przemyslaw Biecek:
survxai: an R package for structure-agnostic explanations of survival models. J. Open Source Softw. 3(31): 961 (2018) - [j7]Mateusz Staniak, Przemyslaw Biecek:
Explanations of Model Predictions with live and breakDown Packages. R J. 10(2): 395 (2018) - [c3]Barbara Rychalska, Dominika Basaj, Anna Wróblewska, Przemyslaw Biecek:
How much should you ask? On the question structure in QA systems. BlackboxNLP@EMNLP 2018: 319-321 - [c2]Barbara Rychalska, Dominika Basaj, Anna Wróblewska, Przemyslaw Biecek:
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System. BlackboxNLP@EMNLP 2018: 322-324 - [i7]Mateusz Staniak, Przemyslaw Biecek:
Explanations of model predictions with live and breakDown packages. CoRR abs/1804.01955 (2018) - [i6]Przemyslaw Biecek:
DALEX: explainers for complex predictive models. CoRR abs/1806.08915 (2018) - [i5]Dominika Basaj, Barbara Rychalska, Przemyslaw Biecek, Anna Wróblewska:
How much should you ask? On the question structure in QA systems. CoRR abs/1809.03734 (2018) - [i4]Barbara Rychalska, Dominika Basaj, Przemyslaw Biecek, Anna Wróblewska:
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System. CoRR abs/1809.03740 (2018) - [i3]Alicja Gosiewska, Przemyslaw Biecek:
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic. CoRR abs/1809.07763 (2018) - [i2]Barbara Rychalska, Dominika Basaj, Przemyslaw Biecek:
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems. CoRR abs/1812.02205 (2018) - 2017
- [j6]Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek:
Retrieval and Analysis of Eurostat Open Data with the eurostat Package. R J. 9(1): 385 (2017) - [i1]Agnieszka Sitko, Przemyslaw Biecek:
Merge and Select: Visualization of a likelihood based k-sample adaptive fusing and model selection. CoRR abs/1709.04412 (2017) - 2011
- [j5]Ewa Szczurek, Florian Markowetz, Irit Gat-Viks, Przemyslaw Biecek, Jerzy Tiuryn, Martin Vingron:
Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data. BMC Bioinform. 12: 249 (2011) - [j4]Norbert Dojer, Przemyslaw Biecek, Jerzy Tiuryn:
Bi-Billboard: Symmetrization and Careful Choice of Informant Species Results in Higher Accuracy of Regulatory Element Prediction. J. Comput. Biol. 18(6): 809-819 (2011) - 2010
- [j3]Ewa Szczurek, Przemyslaw Biecek, Jerzy Tiuryn, Martin Vingron:
Introducing Knowledge into Differential Expression Analysis. J. Comput. Biol. 17(8): 953-967 (2010)
2000 – 2009
- 2008
- [c1]Pawel Mackiewicz, Przemyslaw Biecek, Dorota Mackiewicz, Joanna Kiraga, Krystian Baczkowski, Maciej Sobczynski, Stanislaw Cebrat:
Optimisation of Asymmetric Mutational Pressure and Selection Pressure Around the Universal Genetic Code. ICCS (3) 2008: 100-109 - 2007
- [j2]Andreas Baierl, Andreas Futschik, Malgorzata Bogdan, Przemyslaw Biecek:
Locating multiple interacting quantitative trait loci using robust model selection. Comput. Stat. Data Anal. 51(12): 6423-6434 (2007) - [j1]Marta Zawierta, Przemyslaw Biecek, Wojciech Waga, Stanislaw Cebrat:
The role of intragenomic recombination rate in the evolution of population's genetic pool. Theory Biosci. 125(2): 123-132 (2007)
Coauthor Index
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