Does data interpolation contradict statistical optimality?

Add to your list(s) Download to your calendar using vCal

• Alexandre Tsybakov (CREST: Centre de Recherche en Économie et Statistique)
• Friday 29 June 2018, 09:00-09:45
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

STSW04 - Future challenges in statistical scalability

We exhibit estimators that interpolate the data, and yet achieve optimal rates of convergence for the problems of nonparametric regression and prediction with square loss. This curious observation goes against the usual intuition that a good statistical procedure should forego the exact fit to data in favor of a more smooth representation. A motivation for this work is the recent focus within the machine learning community on the out-of-sample performance of neural networks. These flexible models are typically trained to fit the data exactly (either in their sign or in the actual value), and yet they have good prediction behaviour. This talk is based on joint work with Misha Belkin and Sasha Rakhlin.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.