The weighted majority algorithm
Information and Computation
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
Machine Learning - Special issue on context sensitivity and concept drift
Adaptive disk spin—down for mobile computers
Mobile Networks and Applications
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Path kernels and multiplicative updates
The Journal of Machine Learning Research
Adaptive Online Prediction by Following the Perturbed Leader
The Journal of Machine Learning Research
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Necklaces, convolutions, and X + Y
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A fast normalized maximum likelihood algorithm for multinomial data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Tracking the best of many experts
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Combining initial segments of lists
Theoretical Computer Science
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We propose a new way to build a combined list from K base lists, each containing N items. A combined list consists of top segments of various sizes from each base list so that the total size of all top segments equals N. A sequence of item requests is processed and the goal is to minimize the total number of misses. That is, we seek to build a combined list that contains all the frequently requested items. We first consider the special case of disjoint base lists. There, we design an efficient algorithm that computes the best combined list for a given sequence of requests. In addition, we develop a randomized online algorithm whose expected number of misses is close to that of the best combined list chosen in hindsight. We prove lower bounds that show that the expected number of misses of our randomized algorithm is close to the optimum. In the presence of duplicate items, we show that computing the best combined list is NP-hard. We show that our algorithms still apply to a linearized notion of loss in this case. We expect that this new way of aggregating lists will find many ranking applications.