The Strength of Weak Learnability
Machine Learning
On No-Regret Learning, Fictitious Play, and Nash Equilibrium
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Approximate solutions to markov decision processes
Approximate solutions to markov decision processes
Convex Optimization
Online learning in online auctions
Theoretical Computer Science - Special issue: Online algorithms in memoriam, Steve Seiden
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
No-regret learning in convex games
Proceedings of the 25th international conference on Machine learning
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
No-regret learning and a mechanism for distributed multiagent planning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
From external to internal regret
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we investigate what happens when a set of no-regret learners (voters ) merge their respective decisions in each learning iteration to a single, common one in form of a convex combination. We show that an agent (or algorithm) that executes this merged decision in each iteration of the online learning process and each time feeds back a copy of its own reward function to the voters, incurs sublinear regret itself. As a by-product, we obtain a simple method that allows us to construct new no-regret algorithms out of known ones.