6 citations · 17 across the 18 of their papers we have counts for
18 papers
Competitive Market Behavior of LLMs
Pawel Struski, Jakub Swistak, Inez Okulska +1
Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designe…
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…
Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective
Mustafa Cavus, Przemysław Biecek, Julian Tejada +2
This paper introduces a new modeling perspective in the public mental health domain to provide a robust interpretation of the relations between anxiety and depression, and the demo…
Model Science: getting serious about verification, explanation and control of AI systems
Przemyslaw Biecek, Wojciech Samek
The growing adoption of foundation models calls for a paradigm shift from Data Science to Model Science. Unlike data-centric approaches, Model Science places the trained model at t…
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…
Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
Wiktoria Mieleszczenko-Kowszewicz, Beata Bajcar, Jolanta Babiak +3
Be careful what you ask for, you just might get it. This saying fits with the way large language models (LLMs) are trained, which, instead of being rewarded for correctness, are in…
Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective
Mustafa Cavus, Przemyslaw Biecek
The Rashomon effect presents a significant challenge in model selection. It occurs when multiple models achieve similar performance on a dataset but produce different predictions,…
The Dark Patterns of Personalized Persuasion in Large Language Models: Exposing Persuasive Linguistic Features for Big Five Personality Traits in LLMs Responses
Wiktoria Mieleszczenko-Kowszewicz, Dawid Płudowski, Filip Kołodziejczyk +3
This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs,…
Rethinking Visual Counterfactual Explanations Through Region Constraint
Bartlomiej Sobieski, Jakub Grzywaczewski, Bartlomiej Sadlej +2
Visual counterfactual explanations (VCEs) have recently gained immense popularity as a tool for clarifying the decision-making process of image classifiers. This trend is largely m…
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…
Position: Explain to Question not to Justify
Przemyslaw Biecek, Wojciech Samek
Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incom…