Structured Regularization Summer School - 19-22/06/2017

Collection Structured Regularization Summer School - 19-22/06/2017

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Date(s) 28/03/2024
00:00:00 / 00:00:00
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Regularization Methods for Large Scale Machine Learning 1

By Lorenzo Rosasco

Regularization techniques originally developed to solve linear inverse problems can be extended to derive nonparametric machine learning methods. These methods perform well in practice and can be shown to have optimal statistical guarantees, however, computational requirements can prevent application to large scale scenarios. In this talk, we will describe recent attempts to tackle this challenge. Our presentation will be divided in two parts. In the first part, we will discuss so called iterative regularization, aka early stopping regularization. In particular, we will discuss accelerated and stochastic variants of this method and show how they allow to control at the same time the statistical and time complexities of the obtained solutions. In the second part, we will discuss novel regularization schemes obtained combining regularization with stochastic projections. These latter methods allow to control not only the statistical and time complexities of the obtained solutions but also the memory requirements.

Information about the video

  • Date of publication 23/06/2017
  • Institution IHP
  • Format MP4

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