8 citations · 10 across the 7 of their papers we have counts for
8 papers
Scale-Invariant Training for Time Series Foundation Models
Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1
Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series who…
Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models
Li Wenjie, Yash Jangir, Ignacy Stepka +3
Action verbs describe not only the physical outcomes of actions, but also how those actions are performed. Yet action representations in vision-language-action models (VLAs) are ty…
Explaining Concept Drift through the Evolution of Group Counterfactuals
Ignacy Stępka, Jerzy Stefanowski
Machine learning models in dynamic environments often suffer from concept drift, where changes in the data distribution degrade performance. While detecting this drift is a well-st…
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
Ignacy Stępka, Nicholas Gisolfi, Kacper Trębacz +1
We consider the problem of persistent client dropout in asynchronous Decentralized Federated Learning (DFL). Asynchronicity and decentralization obfuscate information about model u…
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
Ignacy Stępka, Lukasz Sztukiewicz, Michał Wiliński +1
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification t…
A SAT-based approach to rigorous verification of Bayesian networks
Ignacy Stępka, Nicholas Gisolfi, Artur Dubrawski
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications. However, in safety-critical domains, the deployment of mach…
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski
Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static sce…
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of…