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 “Designing Long-term Group Fair Policies in Dynamical Systems” co-authored by Miriam Rateike accepted at the NeurIPS23 workshop on “Algorithmic Fairness Through the Lens of Time”
The paper "Designing Long-term Group Fair Policies in Dynamical Systems" co-authored by our PhD student Miriam Rateike was accepted at the NeurIPS23 workshop on "Algorithmic Fairness Through the Lens of Time". It was also selected as one of 4 contributed talks (oral)....
Paper “Causal Normalizing Flows: from theory to practice” accepted as an Oral to Neurips 2023
The paper "Causal Normalizing Flows: from theory to practice" authored by our members Adrián Javaloy, Pablo Sánchez-Martín, and Isabel Valera was accepted as an **Oral** to Neurips 2023.
Paper “Causal Normalizing Flows: from theory to practice” won Best Paper Award at the 6th Workshop on Tractable Probabilistic Modeling
The paper "Causal Normalizing Flows: from theory to practice" authored by our members Adrián Javaloy, Pablo Sánchez-Martín, and Isabel Valera has won the Best Paper Award at the 6th Workshop on Tractable Probabilistic Modeling at UAI 2023.
Members
