Mini-Workshop: Interpolation and Over-parameterization in Statistics and Machine Learning​

  • Mihkail Belkin

    University of California, San Diego, La Jolla, USA
  • Alexandre B. Tsybakov

    Institut Polytechnique de Paris, Palaiseau Cedex, France
  • Fanny Yang

    ETH Zürich, Zürich, Switzerland

Abstract

In recent years it has become clear that, contrary to traditional statistical beliefs, methods that interpolate (fit exactly) the noisy training data, can still be statistically optimal. In particular, this phenomenon of “be- nign overfitting” or “harmless interpolation” seems to be close to the practical regimes of modern deep learning systems, and, arguably, underlies many of their behaviors. This workshop brought together experts on the emerging theory of interpolation in statistical methods, its theoretical foundations and applications to machine learning and deep learning.

Cite this article

Mihkail Belkin, Alexandre B. Tsybakov, Fanny Yang, Mini-Workshop: Interpolation and Over-parameterization in Statistics and Machine Learning​. Oberwolfach Rep. 20 (2023), no. 3, pp. 2359–2376

DOI 10.4171/OWR/2023/41