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Program Schedule
Program Schedule
Saturday, 19th October
Saturday, 19th October
9:00–9:30 - Welcome
9:30–10:30 - Keynote Talk: Eyke Hullermeier
10:30–11:00 - Coffee Break
11:00–12:30 - Session 1: Uncertainty Quantification
12:30–14:00 - Lunch
14:00–15:30 - Session 2: Domain Adaption and OOD
15:30–16:00 - Coffee Break
16:00–17:30 - Open discussion
Sunday, 20th October
Sunday, 20th October
9:30–10:30 - Keynote Talk: Luciano Sanchez
10:30–11:00 - Coffee Break
11:00–12:30 - Session 3: Weakly Supervised Learning
12:30–14:00 - Lunch
14:00–15:30 - Session 4: Credal Sets and Imprecise Data
15:30–16:00 - Coffee Break
16:00–17:30 - Closing
All full paper contributions are assigned 20 minutes of presentation + 10 minutes of questions and dicussion.
All extended abstract contributions (marked as "Abstract" in the program) are assigned 10 minutes of presentation + 5 minutes of questions and discussion.
The workshop room is equipped with a Windows PC (with no possibility to install anything on it) and a projector. Prepare your presentation accordingly.
Keynote Speakers
Keynote Speakers
Eyke Hüllermeier
Eyke Hüllermeier is a full professor at the Institute of Informatics at LMU Munich, Germany, where he heads the Chair of Artificial Intelligence and Machine Learning. He studied mathematics and business computing, received his PhD in computer science from Paderborn University in 1997, and a Habilitation degree in 2002. Prior to joining LMU, he spent two years as a Marie Curie fellow at the IRIT in Toulouse (France) and held professorships at the Universities of Dortmund, Magdeburg, Marburg, and Paderborn.
His research interests are centered around methods and theoretical foundations of artificial intelligence, with a specific focus on machine learning and reasoning under uncertainty. He has published more than 300 articles on these topics in top-tier journals and major international conferences, and several of his contributions have been recognized with scientific awards. He is President of the European Association for Data Science (EuADS) and Editor-in-Chief of Data Mining and Knowledge Discovery, one of the leading journals in the field of AI. He also serves on the editorial board of several other journals, including Machine Learning, Journal of Machine Learning Research, IEEE Computational Intelligence Magazine, Artificial Intelligence Review, and the International Journal of Approximate Reasoning.
Luciano Sánchez
Luciano Sánchez is a professor at the University of Oviedo. Since 2022, he has been the Director of the TotalEnergies Chair of Data Analytics and Artificial Intelligence at the University of Oviedo. He coordinates the research group "Metrology and Models" and is a founding partner of the group's spin-off, Idalia Intelligent Data Analysis S.L. He was a visiting professor at the University of California in Berkeley (1995) and at General Electric (GE Global Research, Schenectady, New York, 1996).
His research includes the theoretical study of algorithms for mathematical modeling of systems and intelligent data analysis, as well as their application to industrial modeling, signal processing, and dimensional metrology, with a special interest in low-quality data. He received the Outstanding Paper Award at the FUZZ-IEEE 2013 congress (Hyderabad, India) and the 1st Engineering Innovation Preis from Rolls-Royce (Germany) in 2013.
Detailed Program
Detailed Program
Saturday, 19th October
Saturday, 19th October
Session 1: Uncertainty Quantification
Leveraging Random Forest Proximities for Localized Uncertainty Quantification - J. Rhodes, S.D. Brown, J.R. Wilkinson
On the Importance of Initialization in Active Learning - V. Margraf, M. Wever, E. Hüllermeier
AutoML-based workflow for DOE comparative Studies: Incorporating Learning from Noisy Data and Dataset Complexity - X. Xu, D. Li, K. Xu, M. Moeckel
Session 2: Domain Adaption and OOD
Integrating Imprecise Data in Generative Models Using Interval-Valued Variational Autoencoders - L. Sanchez, N. Costa, I. Couso, O. Strauss
Domain-invariant Feature-driven Model and Data Selection for Domain Adaptive RUL Prediction in Real-world Scenarios - H. Lee, S Lee, S Ko
When to Accept Automated Predictions and When to Defer to Human Judgment? - D. Sikar
Sunday, 20th October
Sunday, 20th October
Session 3: Weakly Supervised Learning
Information Retrieval for Small and Medium-Sized Enterprise Healthcare Facilities Using Weakly Supervised Large Language Models (Abstract) - N. Neveditsin, P. Lingras, S. Patil, V. Mago
On the Validity of Credal Classifiers - A. Campagner, D. Ciucci
A Semi-supervised Feature Selection for Anomaly Detection - J. Zhang, G. Dobbie, N. Pham
Session 4: Credal Sets and Imprecise Data
Self-Learning from Pairwise Credal Labels - V.M. Bordini, S. Destercke, B. Quost
Data Imputation in the Frequency Domain Using Echo State Networks (Abstract) - L. Sanchez, N. Costa, I. Couso
SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition - H. Algafri, H. Luqman
Credits Header Picture: Javier Pais (licensed under the under the Creative Commons Attribution 2.0 Generic license)
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