13 citations · 14 across the 5 of their papers we have counts for
6 papers
The Role of Hyperparameters in Predictive Multiplicity
Mustafa Cavus, Katarzyna Woźnica, Przemysław Biecek
This paper investigates the critical role of hyperparameters in predictive multiplicity, where different machine learning models trained on the same dataset yield divergent predict…
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
Katarzyna Woźnica, Piotr Wilczyński, Przemysław Biecek
Machine learning applications cover a wide range of predictive tasks in which tabular datasets play a significant role. However, although they often address similar problems, tabul…
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV
Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas +1
For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyper…
Do not explain without context: addressing the blind spot of model explanations
Katarzyna Woźnica, Katarzyna Pękala, Hubert Baniecki +3
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising…
Does imputation matter? Benchmark for predictive models
Katarzyna Woźnica, Przemysław Biecek
Incomplete data are common in practical applications. Most predictive machine learning models do not handle missing values so they require some preprocessing. Although many algorit…
Towards explainable meta-learning
Katarzyna Woźnica, Przemysław Biecek
Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter…