Machine Learning group
The (probabilistic) machine learning group is led by Isabel Valera, Professor of Machine Learning at Saarland University, Adjunct Faculty of the MPI-SWS and research fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS).
We develop cutting-edge trustworthy machine learning methods to be deployed in the real-world. Our research can be broadly categorized in three main topics: fair, interpretable and robust machine learning. We are an active and diverse research team, with interests in a wide range of ML approaches including deep learning, probabilistic modeling, causal inference, time series analysis, and many more.
Our research has a strong societal component and can be applied in a broad range of application domains, from medicine and psychiatry to social and communication systems. As an example, our recent research has focused on algorithmic decision making in several domains, including hiring processes, pre-trial bail, or loan approval.
News
Paper Accepted In Forty-third International Conference on Machine Learning, 2026
Paper title: Holder++: Improving the Quality-Coherence Trade-off in Multimodal VAEs Authors: Huyen Vo, Marı́a Martı́nez-Garcı́a, and Isabel Valera — Link: https://vothuckhanhhuyen.github.io/assets/pdf/Holder_ICML2026.pdf Abstract: Existing approaches for multimodal...
Spotlight Paper Accepted In The 29th International Conference on Artificial Intelligence and Statistics, 2026
The Paper: Hellinger Multimodal Variational Autoencoders Authors: Huyen Vo and Isabel Valera — Link: https://arxiv.org/pdf/2601.06572 Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple...
Paper Accepted at ACM FAccT Conferrence
The paper: First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs Authors: Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz, Isabel Valera — Paper title: First-See-Then-Design: A Multi-Stakeholder View for Optimal...
Members
