6 citations · 17 across the 18 of their papers we have counts for
10 papers · 1 filter
Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa +10
Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security…
Fake or Real: The Impostor Hunt in Texts for Space Operations
Agata Kaczmarek, Dawid Płudowski, Piotr Wilczyński +6
The "Fake or Real" competition hosted on Kaggle (https://www.kaggle.com/competitions/fake-or-real-the-impostor-hunt ) is the second part of a series of follow-up competitions and h…
Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML
Mustafa Cavus, Jan N. van Rijn, Przemysław Biecek
Automated machine learning systems efficiently streamline model selection but often focus on a single best-performing model, overlooking explanation uncertainty, an essential conce…
Trojan Horse Hunt in Time Series Forecasting for Space Operations
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa +6
This competition hosted on Kaggle (https://www.kaggle.com/competitions/trojan-horse-hunt-in-space) is the first part of a series of follow-up competitions and hackathons related to…
System-Embedded Diffusion Bridge Models
Bartlomiej Sobieski, Matthew Tivnan, Yuang Wang +5
Solving inverse problems -- recovering signals from incomplete or noisy measurements -- is fundamental in science and engineering. Score-based generative models (SGMs) have recentl…
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Dawid Płudowski, Francesco Spinnato, Piotr Wilczyński +4
Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual met…
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…
Efficient and Accurate Explanation Estimation with Distribution Compression
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl +1
We discover a theoretical connection between explanation estimation and distribution compression that significantly improves the approximation of feature attributions, importance,…
On the Robustness of Global Feature Effect Explanations
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl +1
We study the robustness of global post-hoc explanations for predictive models trained on tabular data. Effects of predictor features in black-box supervised learning are an essenti…
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
Mustafa Cavus, Przemysław Biecek
Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurat…