Prof. Dr. Niki Kilbertus
Academic Career and Research Areas
Niki Kilbertus and his team investigate the interactions between machine learning algorithms and humans with a focus on ethical consequences and trustworthiness. They currently study identification and estimation of causal effects from observational data in automated decision-making and dynamic environments.
After studying mathematics and physics at the University of Regensburg, Niki Kilbertus obtained his PhD in machine learning in 2020 from the University of Cambridge (UK) in a joint program with the Max Planck Institute for Intelligent Systems. Since 2020 he is a Young Investigator Group Leader with Helmholtz AI at the Helmholtz Center Munich. Since 2021 he is Assistant Professor at TUM and continues to lead his group at Helmholtz AI.
Key Publications (all publications)
Hron J, Krauth K, Jordan MI, Kilbertus N: "On component interactions in two-stage recommender systems”. Advances in Neural Information Processing Systems. 2021.Abstract
Kilbertus N, Kusner MJ, Silva R: "A class of algorithms for general instrumental variable models”. Advances in Neural Information Processing Systems. 2020.Abstract
Kilbertus N, Gomez-Rodriguez M, Schölkopf B, Muandet K, Valera I: "Fair decisions despite imperfect predictions”. International Conference on Artificial Intelligence and Statistics. 2020.Abstract
Kilbertus N, Gascón A, Kusner MJ, Veale M, Gummadi KP, Weller A: “Blind Justice: Fairness with Encrypted Sensitive Attributes”. International Conference on Machine Learning. 2018.Abstract
Kilbertus, N, Rojas-Carulla M, Parascandolo G, Hardt M, Janzing D, Schölkopf B: “Avoiding discrimination through causal reasoning”. Advances in Neural Information Processing Systems. 2017.Abstract