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cs.LG2025
Explaining Concept Drift through the Evolution of Group Counterfactuals
Ignacy StÄpka, 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…
cs.LG2025
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
Ignacy StÄpka, Nicholas Gisolfi, Kacper TrÄbacz +1
We consider the problem of persistent client dropout in asynchronous Decentralized Federated Learning (DFL). Asynchronicity and decentralization obfuscate information about model u…
cs.LG2025
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
Ignacy StÄpka, 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…