Sebastian Bordt

I'm a Ph.D. candidate in the Theory of Machine Learning group under the supervision of Prof. Dr. Ulrike von Luxburg.

Research Interests. I'm broadly interested in the analysis of machine learning algorithms and their societal impact. My current work focuses on explainable machine learning and human-machine interaction, particulary decision making in applications such as health care. In these applications, I'm intersted in how human decision makers can be optimally supported by machine learning. My recent research also focuses on the analysis of post-hoc algorithms for explainabile machine learning.

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Preprints

• Sebastian Bordt, Ulrike von Luxburg From Shapley Values to Generalized Additive Models and back, arxiv, arxiv preprint

• Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg The Manifold Hypothesis for Gradient-Based Explanations, arxiv, arxiv preprint

Peer-Reviewed Publications

• Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg, Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts, arxiv, FAccT 2022
• Sebastian Bordt, Ulrike von Luxburg, A Bandit Model for Human-Machine Decision Making with Private Information and Opacity, arxiv, AISTATS 2022
• Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar, Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models, pdf, AISTATS 2021, Oral presentation

Awards