A randomization rule for selecting forecasts
Operations Research
The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
On-line learning and the metrical task system problem
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Online computation and competitive analysis
Online computation and competitive analysis
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Adapting to a reliable network path
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Prediction, Learning, and Games
Prediction, Learning, and Games
Playing games with approximation algorithms
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Online linear optimization and adaptive routing
Journal of Computer and System Sciences
From External to Internal Regret
The Journal of Machine Learning Research
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Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single decision maker, a near optimal behavior under fairly adversarial assumptions. I will discuss a recent extensions of the classical regret minimization model, which enable to handle many different settings related to job scheduling, and guarantee the near optimal online behavior.