Competitive algorithms for server problems
Journal of Algorithms
An optimal on-line algorithm for metrical task system
Journal of the ACM (JACM)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
A polylog(n)-competitive algorithm for metrical task systems
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
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
Randomized algorithms for metrical task systems
Theoretical Computer Science
On approximating arbitrary metrices by tree metrics
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Machine Learning - Special issue on context sensitivity and concept drift
Regret bounds for prediction problems
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
On-line Learning and the Metrical Task System Problem
Machine Learning
A Decomposition Theorem for Task Systems and Bounds for Randomized Server Problems
SIAM Journal on Computing
A tight bound on approximating arbitrary metrics by tree metrics
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Better Algorithms for Unfair Metrical Task Systems and Applications
SIAM Journal on Computing
Prediction, Learning, and Games
Prediction, Learning, and Games
Topology matters: smoothed competitiveness of metrical task systems
Theoretical Computer Science
Uniform metrical task systems with a limited number of states
Information Processing Letters
A Primal-Dual Randomized Algorithm for Weighted Paging
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
The Design of Competitive Online Algorithms via a Primal: Dual Approach
Foundations and Trends® in Theoretical Computer Science
Towards the randomized k-server conjecture: a primal-dual approach
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete 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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We address the problem of constructing randomized online algorithms for the Metrical Task Systems (MTS) problem on a metric δ against an oblivious adversary. Restricting our attention to the class of "work-based" algorithms, we provide a framework for designing algorithms that uses the technique of regularization. For the case when δ is a uniform metric, we exhibit two algorithms that arise from this framework, and we prove a bound on the competitive ratio of each. We show that the second of these algorithms is ln n + O(log log n) competitive, which is the current state-of-the art for the uniform MTS problem.