120 citations · 184 across the 7 of their papers we have counts for
7 papers
Mining United Nations General Assembly Debates
Mateusz Grzyb, Mateusz Krzyziński, Bartłomiej Sobieski +4
This project explores the application of Natural Language Processing (NLP) techniques to analyse United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient…
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek +3
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has becom…
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
Katarzyna Kobylińska, Mateusz Krzyziński, Rafał Machowicz +2
The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning…
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein +3
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread a…
HADES: Homologous Automated Document Exploration and Summarization
Piotr Wilczyński, Artur Żółkowski, Mateusz Krzyziński +5
This paper introduces HADES, a novel tool for automatic comparative documents with similar structures. HADES is designed to streamline the work of professionals dealing with large…
Climate Policy Tracker: Pipeline for automated analysis of public climate policies
Artur Żółkowski, Mateusz Krzyziński, Piotr Wilczyński +5
The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual anal…
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki +1
Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too…