COLT '90 Proceedings of the third annual workshop on Computational learning theory
Journal of the ACM (JACM)
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
On the Absence of Predictive Complexity for Some Games
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Mixability and the Existence of Weak Complexities
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Adaptive Online Prediction by Following the Perturbed Leader
The Journal of Machine Learning Research
Prediction, Learning, and Games
Prediction, Learning, and Games
Prediction With Expert Advice For The Brier Game
The Journal of Machine Learning Research
Prediction with expert advice under discounted loss
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Weak aggregating algorithm for the distribution-free perishable inventory problem
Operations Research Letters
Mixability is bayes risk curvature relative to log loss
The Journal of Machine Learning Research
| Hi-index | 0.00 |
This paper resolves the problem of predicting as well as the best expert up to an additive term of the order o(n), where n is the length of a sequence of letters from a finite alphabet. We call the games that permit this weakly mixable and give a geometrical characterisation of the class of weakly mixable games. Weak mixability turns out to be equivalent to convexity of the finite part of the set of superpredictions. For bounded games we introduce the Weak Aggregating Algorithm that allows us to obtain additive terms of the form Cn.