Search Results for author: Theodoros Evgeniou

Found 6 papers, 1 papers with code

Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem

no code implementations24 Jun 2023 Sofie Goethals, David Martens, Theodoros Evgeniou

Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made.

Explainable Artificial Intelligence (XAI)

Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data

no code implementations10 Mar 2020 Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet

Machine learning models on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret.

Counterfactual Explanation Algorithms for Behavioral and Textual Data

3 code implementations4 Dec 2019 Yanou Ramon, David Martens, Foster Provost, Theodoros Evgeniou

This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets.

counterfactual Counterfactual Explanation

Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease

no code implementations21 Sep 2017 Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot

The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features.

Benchmarking Classification +1

Link Discovery using Graph Feature Tracking

no code implementations NeurIPS 2010 Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis

We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.

Matrix Completion

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