8 citations · 10 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…