Variable Metric Stochastic Approximation Theory

P. Sunehag, J. Trumpf, S. Vishwanathan, and N. N. Schraudolph. Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl. Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566, Journal of Machine Learning Research, Clearwater Beach, Florida, 2009.

Download

pdf djvu ps.gz
167.0kB   80.5kB   182.6kB  

Abstract

We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online versions of the BFGS algorithm and its limited-memory LBFGS variant. We also discuss the implications of our results for learning from expert advice.

BibTeX Entry

@inproceedings{SunTruVisSch09,
     author = {Peter Sunehag and Jochen Trumpf and
               S.~V.~N. Vishwanathan and Nicol N. Schraudolph},
      title = {\href{http://nic.schraudolph.org/pubs/SunTruVisSch09.pdf}{
               Variable Metric Stochastic Approximation Theory}},
     editor = {David {van Dyk} and Max Welling},
      pages = {560--566},
  booktitle = {Proc.\ 12$^{th}$ Intl.\ Conf.\ Artificial
               Intelligence and Statistics (AIstats)},
    address = {Clearwater Beach, Florida},
     volume =  5,
     series = {Workshop and Conference Proceedings},
  publisher =  jmlr,
       year =  2009,
   b2h_type = {Top Conferences},
  b2h_topic = {>Quasi-Newton Methods},
   abstract = {
    We provide a variable metric stochastic approximation theory.
    In doing so, we provide a convergence theory for a large class
    of online variable metric methods including the recently
    introduced online versions of the BFGS algorithm and its
    limited-memory LBFGS variant. We also discuss the implications
    of our results for learning from expert advice.
}}

Generated by bib2html.pl (written by Patrick Riley) on Thu Sep 25, 2014 12:00:33