4 citations · 6 across the 9 of their papers we have counts for
12 papers
Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures
Paweł Lorek, Rafał Nowak, Rafał Topolnicki +2
We study estimating rare-event probabilities with and general $g : \mathbb{R}^d \to \m…
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…
CEL: Comprehensive Counterfactual Explanations Library and Benchmark
Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek +1
Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's predict…
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…
CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
Oleksii Furman, Patryk Marszałek, Jan Masłowski +3
Counterfactual explanations (CFs) provide human-interpretable insights into model's predictions by identifying minimal changes to input features that would alter the model's output…
Reducing Estimation Uncertainty Using Normalizing Flows and Stratification
Paweł Lorek, Rafał Nowak, Rafał Topolnicki +3
Estimating the expectation of a real-valued function of a random variable from sample data is a critical aspect of statistical analysis, with far-reaching implications in various a…
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…
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control
Oleksii Furman, Ulvi Movsum-zada, Patryk Marszalek +2
Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learnin…
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski +1
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic f…