The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A game of prediction with expert advice
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
Prediction, Learning, and Games
Prediction, Learning, and Games
Regret Minimization Under Partial Monitoring
Mathematics of Operations Research
Minimizing regret with label efficient prediction
IEEE Transactions on Information Theory
Regret to the best vs. regret to the average
Machine Learning
Better algorithms for benign bandits
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
The follow perturbed leader algorithm protected from unbounded one-step losses
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm
The Journal of Machine Learning Research
Better Algorithms for Benign Bandits
The Journal of Machine Learning Research
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Adaptive and optimal online linear regression on l1-balls
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Competing against the best nearest neighbor filter in regression
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Regret minimization algorithms for pricing lookback options
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Pricing exotic derivatives using regret minimization
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
Lower bounds on individual sequence regret
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Online Multiple Kernel Classification
Machine Learning
Sparsity regret bounds for individual sequences in online linear regression
The Journal of Machine Learning Research
Adaptive and optimal online linear regression on ℓ1-balls
Theoretical Computer Science
Combining initial segments of lists
Theoretical Computer Science
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This work studies external regret in sequential prediction games with both positive and negative payoffs. External regret measures the difference between the payoff obtained by the forecasting strategy and the payoff of the best action. In this setting, we derive new and sharper regret bounds for the well-known exponentially weighted average forecaster and for a second forecaster with a different multiplicative update rule. Our analysis has two main advantages: first, no preliminary knowledge about the payoff sequence is needed, not even its range; second, our bounds are expressed in terms of sums of squared payoffs, replacing larger first-order quantities appearing in previous bounds. In addition, our most refined bounds have the natural and desirable property of being stable under rescalings and general translations of the payoff sequence.