Accession Number:

ADA456058

Title:

No-Regret Algorithms for Structured Prediction Problems

Descriptive Note:

Research paper

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2005-12-21

Pagination or Media Count:

46.0

Abstract:

No-regret algorithms are a popular class of online learning rules. Unfortunately, most no-regret algorithms assume that the set Y of allowable hypotheses is small and discrete. Instead, the authors consider prediction problems where Y has internal structure Y might be the set of strategies in a game like poker, the set of paths in a graph, or the set of configurations of a data structure like a rebalancing binary search tree or Y might be a given convex set the online convex programming problem, or, in general, an arbitrary bounded set. They derive a family of no-regret learning rules, called Lagrangian Hedging algorithms, to take advantage of this structure. Their algorithms are a direct generalization of known no-regret learning rules, like weighted majority and external-regret matching. In addition to proving regret bounds, they demonstrate one of their algorithms learning to play one-card poker.

Subject Categories:

  • Statistics and Probability
  • Computer Programming and Software
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE