Domain adaptation in regression

Corinna Cortes, Mehryar Mohri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a series of new results for domain adaptation in the regression setting. We prove that the discrepancy is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by a universal kernel such as the Gaussian kernel. We give new pointwise loss guarantees based on the discrepancy of the empirical source and target distributions for the general class of kernel-based regularization algorithms. These bounds have a simpler form than previous results and hold for a broader class of convex loss functions not necessarily differentiable, including L q losses and the hinge loss. We extend the discrepancy minimization adaptation algorithm to the more significant case where kernels are used and show that the problem can be cast as an SDP similar to the one in the feature space. We also show that techniques from smooth optimization can be used to derive an efficient algorithm for solving such SDPs even for very high-dimensional feature spaces. We have implemented this algorithm and report the results of experiments demonstrating its benefits for adaptation and show that, unlike previous algorithms, it can scale to large data sets of tens of thousands or more points.

Original languageEnglish (US)
Title of host publicationAlgorithmic Learning Theory - 22nd International Conference, ALT 2011, Proceedings
Pages308-323
Number of pages16
DOIs
StatePublished - 2011
Event22nd International Conference on Algorithmic Learning Theory, ALT 2011 - Espoo, Finland
Duration: Oct 5 2011Oct 7 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6925 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Algorithmic Learning Theory, ALT 2011
CountryFinland
CityEspoo
Period10/5/1110/7/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Cortes, C., & Mohri, M. (2011). Domain adaptation in regression. In Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Proceedings (pp. 308-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6925 LNAI). https://doi.org/10.1007/978-3-642-24412-4_25