2 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanations
Marcin Kostrzewa, Maciej Zięba
Robust counterfactual explanations promise recourse that still works after the model behind it changes. Whether they keep that promise depends on what the change is. A small pertur…
Counterfactual Explanations Under Concept Drift
Marcin Kostrzewa, Jerzy Stefanowski, Maciej Zięba
Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. This assumption breaks in evolv…
V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction
Marcin Kostrzewa, Sebastian Tomczak, Roman Furman +5
Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain…
A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
Marcin Kostrzewa, Maciej Zięba, Jerzy Stefanowski
Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Existing methods for generating…
Towards plausibility in time series counterfactual explanations
Marcin Kostrzewa, Krzysztof Galus, Maciej Zięba
We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in…
Are Foundation Models Useful for Bankruptcy Prediction?
Marcin Kostrzewa, Oleksii Furman, Roman Furman +2
Foundation models have shown promise across various financial applications, yet their effectiveness for corporate bankruptcy prediction remains systematically unevaluated against e…