Surprising Failures of Standard Practices in ML When the Sample Size Is Small

24.03.2023 11:15 – 12:15

RESEARCH CENTER FOR STATISTICS SEMINAR / 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.

Lieu

Bâtiment: Uni Mail

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Boulevard du Pont-d'Arve 40
1205 Geneva

Room M 3393, 3rd floor

Organisé par

Faculté d'économie et de management
Research Center for Statistics

Intervenant-e-s

Fanny YANG, ETH Zurich, Switzerland

entrée libre

Classement

Catégorie: Séminaire

Plus d'infos

www.unige.ch/gsem/en/research/seminars/rcs/

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