Amortized efficiency of list update and paging rules
Communications of the ACM
Self-adjusting binary search trees
Journal of the ACM (JACM)
Lower bounds for accessing binary search trees with rotations
SIAM Journal on Computing
A lower bound for randomized list update algorithms
Information Processing Letters
The weighted majority algorithm
Information and Computation
A combined BIT and TIMESTAMP algorithm for the list update problem
Information Processing Letters
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Online computation and competitive analysis
Online computation and competitive analysis
On-line Learning and the Metrical Task System Problem
Machine Learning
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Efficient algorithms for universal portfolios
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Machine learning in metrical task systems and other on-line problems
Machine learning in metrical task systems and other on-line problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Internal synchronization of drift-constraint clocks in ad-hoc sensor networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
O(log log n)-competitive dynamic binary search trees
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Chain-splay trees, or, how to achieve and prove loglogN-competitiveness by splaying
Information Processing Letters
How to splay for loglogn-competitiveness
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
A tale of two metrics: simultaneous bounds on competitiveness and regret
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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Adaptive data structures form a central topic of on-line algorithms research, beginning with the results of Sleator and Tarjan showing that splay trees achieve static optimality for search trees, and that Move-to-Front is constant competitive for the list update problem [ST85a, ST85b]. This paper is inspired by the observation that one can in fact achieve a 1 + ε ratio against the best static object in hindsight for a wide range of data structure problems via "weighted experts" techniques from Machine Learning, if computational decision-making costs are not considered.In this paper, we give two results. First, we show that for the case of lists, we can achieve a 1 + ε ratio with respect to the best static list in hindsight, by a simple efficient algorithm. This algorithm can then be combined with existing results to simultaneously achieve good static and dynamic bounds. Second, for trees, we show a (computationally inefficient) algorithm that achieves what we call "dynamic search optimality": dynamic optimality if we allow the online algorithm to make free rotations after each request. We hope this to be a step towards solving the longstanding open problem of achieving true dynamic optimality for trees.