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
Two Papers Accepted At ICLR 2026 Conference
Paper Title: A Probabilistic Hard Concept Bottleneck for Steerable Generative Models Authors: María Martínez-García, Ricardo Vazquez Alvarez, Alejandro Lancho, Pablo M. Olmos, Isabel Valera Link: https://openreview.net/pdf/d3f49948fcc28f692fbc2c3bf36afe5a0ecc7853.pdf...
Two Papers Accepted At ICML 2026 Conference
Paper Title: Long-term Fairness with Selective Labels Authors: Giovani Valdrighi, Isabel Valera, and Marcos M. Raimundo. Link: https://icml.cc/virtual/2026/poster/63248 Focus: Investigates the design of decision-making algorithms that ensure fairness over long-term...
Two Papers Accepted At ACM FAccT 2026 Conference
Paper Title: From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse Authors: Budde, Lena Marie; Majumdar, Ayan; Uth, Richard; Langer, Markus; Valera, Isabel Link: https://arxiv.org/abs/2604.08030 Focus: Explores individual...
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