Faculty
Dr. Rebekka Burkholz is a tenured faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken. The focus of her research is relational machine learning. Her main goal is to gain a theoretical understanding of deep learning from a complex network perspective and improve contemporary algorithms based on these insights. Her favourite applications are currently in molecular biology.
From 2019-2021, she was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health working with John Quackenbush. Before that, she enjoyed postdoctoral research at ETH Zurich, from 2017-2018 at the Institute for Machine Learning with Joachim Buhmann and from 2016-2017 at the Chair of Systems Design with Frank Schweitzer. Her PhD research from 2013-2016 at the ETH Risk Center was supervised by Frank Schweitzer and co-supervised by Hans J. Herrmann. Her thesis on systemic risk won the Zurich Dissertation Prize and her work on international maize trade received the CSF Best Contribution Award. She studied Mathematics and Physics at TU Darmstadt.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Bridging Domains through Subspace-Aware Model Merging
iScienceSex differences in gene regulation and its impact on cancer incidence
International Conference on Learning Representations (ICLR)
International Conference on Learning Representations (ICLR)
Never Saddle Down for Reparameterized Steepest Descent as Mirror Flow
International Conference on Learning Representations (ICLR)
Hyperbolic Aware Minimization: Implicit Bias for Sparsity
International Conference on Learning Representations (ICLR)
When Shift Happens - Confounding Is to Blame
International Conference on Learning Representations (ICLR)
Boosting for Predictive Sufficiency
International Conference on Artificial Intelligence and Statistics (AISTATS)
Frequency-Based Hyperparameter Selection in Games
Conference on Neural Information Processing Systems (NeurIPS)
Pay Attention to Small Weights
Conference on Neural Information Processing Systems (NeurIPS)
The Graphon Limit Hypothesis: Understanding Neural Network Pruning via Infinite Width Analysis