Przemyslaw Biecek

Wojciech Kretowicz

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Interpretable Meta-Score for Model Performance: Extended Abstract.

[BibT_eX]

[DOI]

Proceedings of the ECML/PKDD Workshop on Meta-Knowledge Transfer, 2022

Explainable expected goal models for performance analysis in football analytics.

[BibT_eX]

[DOI]

Mustafa Cavus

Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

Manipulating SHAP via Adversarial Data Perturbations (Student Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies.

[BibT_eX]

[DOI]

Pattern Recognit., 2021

dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Interpretable segmentation of medical free-text records based on word embeddings.

[BibT_eX]

[DOI]

Adam Gabriel Dobrakowski

J. Intell. Inf. Syst., 2021

Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering.

[BibT_eX]

[DOI]

Anna Kozak

Decis. Support Syst., 2021

LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations.

[BibT_eX]

[DOI]

Weronika Hryniewska

Adrianna Grudzien

CoRR, 2021

MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence.

[BibT_eX]

[DOI]

CoRR, 2021

Do not explain without context: addressing the blind spot of model explanations.

[BibT_eX]

[DOI]

CoRR, 2021

Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models.

[BibT_eX]

[DOI]

CoRR, 2021

Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure.

[BibT_eX]

[DOI]

Katarzyna Pekala

CoRR, 2021

fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation.

[BibT_eX]

[DOI]

Jakub Wisniewski

CoRR, 2021

Towards Explainable Meta-learning.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Supporting Doctor's Decisions Based on Electronic Medical Documentation in Polish.

[BibT_eX]

[DOI]

Wojciech Jaworski

Adam Gabriel Dobrakowski

Malgorzata Marciniak

Agnieszka Mykowiecka

Agnieszka Morusiewicz

Joanna Przetacka

Lukasz Kaminski

Proceedings of the MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation, 2021

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 28th International Conference, 2021

Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts.

[BibT_eX]

[DOI]

Proceedings of the 16th International Conference on Document Analysis and Recognition, 2021

Responsible Prediction Making of COVID-19 Mortality (Student Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Do not repeat these mistakes - a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection.

[BibT_eX]

[DOI]

CoRR, 2020

Landscape of R packages for eXplainable Artificial Intelligence.

[BibT_eX]

[DOI]

Szymon Maksymiuk

CoRR, 2020

MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets.

[BibT_eX]

[DOI]

Wojciech Kretowicz

CoRR, 2020

Does imputation matter? Benchmark for predictive models.

[BibT_eX]

[DOI]

CoRR, 2020

Interpretable Meta-Measure for Model Performance.

[BibT_eX]

[DOI]

CoRR, 2020

The Grammar of Interactive Explanatory Model Analysis.

[BibT_eX]

[DOI]

CoRR, 2020

Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout.

[BibT_eX]

[DOI]

CoRR, 2020

Towards better understanding of meta-features contributions.

[BibT_eX]

[DOI]

CoRR, 2020

Lifting Interpretability-Performance Trade-off via Automated Feature Engineering.

[BibT_eX]

[DOI]

CoRR, 2020

KRAB ZNF explorer - the online tool for the exploration of the transcriptomic profiles of KRAB-ZNF factors in The Cancer Genome Atlas.

[BibT_eX]

[DOI]

Bioinform., 2020

What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations.

[BibT_eX]

[DOI]

Michal Kuzba

Proceedings of the ECML PKDD 2020 Workshops, 2020

Explainable AI Methods - A Brief Overview.

[BibT_eX]

[DOI]

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

The Landscape of R Packages for Automated Exploratory Data Analysis.

[BibT_eX]

[DOI]

Mateusz Staniak

R J., 2019

auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics.

[BibT_eX]

[DOI]

R J., 2019

modelDown: automated website generator with interpretable documentation for predictive machine learning models.

[BibT_eX]

[DOI]

J. Open Source Softw., 2019

pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python.

[BibT_eX]

[DOI]

Michal Kuzba

Ewa Baranowska

J. Open Source Softw., 2019

modelStudio: Interactive Studio with Explanations for ML Predictive Models.

[BibT_eX]

[DOI]

J. Open Source Softw., 2019

EPP: interpretable score of model predictive power.

[BibT_eX]

[DOI]

CoRR, 2019

Model Development Process.

[BibT_eX]

[DOI]

CoRR, 2019

Clustering of Medical Free-Text Records Based on Word Embeddings.

[BibT_eX]

[DOI]

Adam Gabriel Dobrakowski

CoRR, 2019

iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models.

[BibT_eX]

[DOI]

CoRR, 2019

SAFE ML: Surrogate Assisted Feature Extraction for Model Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Models in the Wild: On Corruption Robustness of Neural NLP Systems.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 26th International Conference, 2019

Named Entity Recognition - Is There a Glass Ceiling?

[BibT_eX]

[DOI]

Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019

Explainable Machine Learning for Modeling of Early Postoperative Mortality in Lung Cancer.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems, 2019

2018

Explanations of Model Predictions with live and breakDown Packages.

[BibT_eX]

[DOI]

Mateusz Staniak

R J., 2018

survxai: an R package for structure-agnostic explanations of survival models.

[BibT_eX]

[DOI]

Aleksandra Grudziaz

J. Open Source Softw., 2018

DALEX: Explainers for Complex Predictive Models in R.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2018

Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems.

[BibT_eX]

[DOI]

Barbara Rychalska

Dominika Basaj

CoRR, 2018

auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic.

[BibT_eX]

[DOI]

CoRR, 2018

DALEX: explainers for complex predictive models.

[BibT_eX]

[DOI]

CoRR, 2018

Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System.

[BibT_eX]

[DOI]

Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, 2018

How much should you ask? On the question structure in QA systems.

[BibT_eX]

[DOI]

Proceedings of the Workshop: Analyzing and Interpreting Neural Networks for NLP, 2018

2017

Retrieval and Analysis of Eurostat Open Data with the eurostat Package.

[BibT_eX]

[DOI]

R J., 2017

Merge and Select: Visualization of a likelihood based k-sample adaptive fusing and model selection.

[BibT_eX]

[DOI]

Agnieszka Sitko

CoRR, 2017

2011

Bi-Billboard: Symmetrization and Careful Choice of Informant Species Results in Higher Accuracy of Regulatory Element Prediction.

[BibT_eX]

[DOI]

Norbert Dojer