Welcome to the website of the Statistical Machine Learning group at ETH Zurich!
We are a group of curious minds from different parts of the world who study exciting questions in the intersection of statistics and machine learning. On a high level, we like to develop theoretical understanding for methodological advancements and vice versa.
Most projects in our group revolve around overparameterized models in high dimensions (linear or neural networks). Please have a look at our recent papers to get a better sense for our research interests. For example, we currently study the effects of inductive bias on interpolating models for standard and robust generalization, semi-supervised learning and memorization in the context of worst-group accuracy, and work on developing interpretable models that simultaneously learn high-level concepts.
|Jun 9, 2022||Slides for today’s talk at the online 1W-MINDS seminar on a new bias-variance trade-off by interpolators induced by the strength of inductive bias|
|May 16, 2022||The papers on fairness and privacy trade-off (oral presentation) and semi-supervised novelty detection were accepted to UAI ‘22, and the paper on close to optimal rates for noisy interpolators accepted to ICML ‘22|
|Mar 9, 2022||Slides for today’s talk at the MSRI Workshop giving an overview over our recent works that prove how noisy interpolation can have close to 1/n rates with the right inductive bias but exhibits peculiar phenomena in the context of robustness.|
|Mar 8, 2022||Our group website is finally online!|
|Jan 15, 2022||We can now be found on twitter!|