Time Title Author(s) Duration
2.30-2.35 Welcome and Introduction Christos Diou & Vasilis Gkolemis 5 minutes
2.35-2.50 Short introduction on the intersection of uncertainty and explainability in machine learning Christos Diou 15'
2.50-3.10 Using Stochastic Methods to Setup High Precision Experiments Kristina Veljković (17' presentation + 3' questions)
3.10-3.30 Using Part-based Representations for Explainable Deep Reinforcement Learning Manos Kirtas (17' presentation + 3' questions)
3.30-4.00 Explaining an image classifier with a GAN conditioned by uncertainty Adrien Le Coz 7 minutes
Identifying Trends in Feature Attributions during Training of Neural Networks Elena Terzieva 7 minutes
Relation of Activity and Confidence when Training Deep Neural Networks Valerie Krug 7 minutes
Temperature scaling for reliable uncertainty estimation: Application to automatic music genre classification Hanna Lukashevich 7 minutes
Coffee Break
4.30-4.50 Explainable Learning with Hierarchical Online Deterministic Annealing Christos Mavridis (17' presentation + 3' questions)
4.50-5.10 Regionally Additive Models: Explainable-by-design models minimizing feature interactions Vasilis Gkolemis (17' presentation + 3' questions)
5.10-5.45 FALE: Fairness aware ALE plots for auditing bias in subgroups Giorgos Giannopoulos 7 minutes
Improving the Validity of Decision Trees as Explanations Jiří Němeček 7 minutes
Towards Explainability in Monocular Depth Estimation Vasileios Arampatzakis 7 minutes
Explaining uncertainty in AI for clinical decision support systems Elisabeth Heremans 7 minutes
Designing a Method to Identify Explainability Requirements in Cancer Research Didier Domínguez 7 minutes
5.45-6.00 Poster session - Poster dimensions (75x200 cm) double-side 15 minutes