[Statlist] Research Seminar in Statistics | *FRIDAY 24 MARCH 2023* | GSEM, University of Geneva

[gsem-support-instituts]

Dear all,

We are pleased to invite you to our next Research Seminar, organized by Professor Sebastian Engelke on behalf of the Research Center for Statistics. (https://www.unige.ch/gsem/en/research/institutes/rcs/team/)

FRIDAY 24 MARCH 2023 at 11:15 am, Uni Mail M 3393 & Online
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192

Surprising Failures of Standard Practices in ML When the Sample Size Is Small
Fanny YANG, ETH Zurich, Switzerland
https://sml.inf.ethz.ch/group/fannyy/

ABSTRACT:
In this talk, we discuss two failure cases of common practices that are typically believed to improve on vanilla methods: (i) adversarial training can lead to worse robust accuracy than standard training (ii) active learning can lead to a worse classifier than a model trained using uniform samples. In particular, we can prove both mathematically and empirically, that such failures can happen in the small-sample regime. We discuss high-level explanations derived from the theory, that shed light on the causes of these phenomena in practice.

BIOGRAPHY:

Fanny Yang is an Assistant Professor of Computer Science at ETH Zurich. She received her Ph.D. in EECS from University of California, Berkeley in 2018 and was a postdoctoral fellow at Stanford University and ETH-ITS in 2019. Her current research interests include methodological and theoretical advances for problems that arise from distribution shift or adversarial robustness requirements, studying the (robust) generalization ability of overparameterized models for high-dimensional data, and interpretability/explainability of neural networks.

> View the Research Seminar agenda: https://www.unige.ch/gsem/en/research/seminars/rcs/

Regards,


Marie-Madeleine

Marie-Madeleine Novo
Assistant to the Research Institutes
gsem-support-instituts using unige.ch