activity
20212026
most citedGLANCE: Global Actions in a Nutshell for Counterfactual Explainability

3 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering

Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas +1

Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the tr…

cs.AI2025

EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection

Georgios Kaoukis, Ioannis Aris Koufopoulos, Eleni Psaroudaki +4

As AI and web agents become pervasive in decision-making, it is critical to design intelligent systems that not only support sustainability efforts but also guard against misinform…

cs.LG2024★ 3 cited

GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas +9

The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods…

cs.LG2024★ 1 cited

Fairness in Ranking: Robustness through Randomization without the Protected Attribute

Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek +1

There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recomm…

cs.LG2023★ 3 cited

Fairness Aware Counterfactuals for Subgroups

Loukas Kavouras, Konstantinos Tsopelas, Giorgos Giannopoulos +6

In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisit…

cs.LG2021

Label Ranking through Nonparametric Regression

Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki

Label Ranking (LR) corresponds to the problem of learning a hypothesis that maps features to rankings over a finite set of labels. We adopt a nonparametric regression approach to L…