Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Multitask Learning with Expert Advice

Jacob Duncan Abernethy, Peter Bartlett and Alexander Rakhlin

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2007-20
January 28, 2007

http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-20.pdf

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the "ideal" algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.


BibTeX citation:

@techreport{Abernethy:EECS-2007-20,
    Author = {Abernethy, Jacob Duncan and Bartlett, Peter and Rakhlin, Alexander},
    Title = {Multitask Learning with Expert Advice},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2007},
    Month = {Jan},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-20.html},
    Number = {UCB/EECS-2007-20},
    Abstract = {We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the "ideal" algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.}
}

EndNote citation:

%0 Report
%A Abernethy, Jacob Duncan
%A Bartlett, Peter
%A Rakhlin, Alexander
%T Multitask Learning with Expert Advice
%I EECS Department, University of California, Berkeley
%D 2007
%8 January 28
%@ UCB/EECS-2007-20
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-20.html
%F Abernethy:EECS-2007-20