Amortized efficiency of list update and paging rules
Communications of the ACM
Amortized analyses of self-organizing sequential search heuristics
Communications of the ACM - Lecture notes in computer science Vol. 174
Self-adjusting binary search trees
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
A survey of adaptive sorting algorithms
ACM Computing Surveys (CSUR)
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
The weighted majority algorithm
Information and Computation
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
An introduction to computational learning theory
An introduction to computational learning theory
Journal of the ACM (JACM)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Self-Organizing Binary Search Trees
Journal of the ACM (JACM)
Journal of Algorithms
Self-customized BSP trees for collision detection
Computational Geometry: Theory and Applications - special issue on virtual reality
On self-organizing sequential search heuristics
Communications of the ACM
Sublinear time approximate clustering
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Polynomial-time approximation schemes for geometric min-sum median clustering
Journal of the ACM (JACM)
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Machine Learning
Static optimality and dynamic search-optimality in lists and trees
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
SIAM Journal on Computing
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Self-Organizing Data Structures
Developments from a June 1996 seminar on Online algorithms: the state of the art
Approximation schemes for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Smaller coresets for k-median and k-means clustering
SCG '05 Proceedings of the twenty-first annual symposium on Computational geometry
Online convex optimization in the bandit setting: gradient descent without a gradient
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Sorting and Searching (Eatcs Monographs on Theoretical Computer Science)
Sorting and Searching (Eatcs Monographs on Theoretical Computer Science)
Self-improving algorithms for delaunay triangulations
Proceedings of the twenty-fourth annual symposium on Computational geometry
Technical opinion: Steering self-learning distance algorithms
Communications of the ACM - Scratch Programming for All
Self-improving algorithms for convex hulls
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Self-improving algorithms for coordinate-wise maxima
Proceedings of the twenty-eighth annual symposium on Computational geometry
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We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an arbitrary, unknown input distribution. We give such self-improving algorithms for sorting and clustering. The highlights of this work: (i) a sorting algorithm with optimal expected limiting running time; and (ii) a k-median algorithm over the Hamming cube with linear expected limiting running time. In all cases, the algorithm begins with a learning phase during which it adjusts itself to the input distribution (typically in a logarithmic number of rounds), followed by a stationary regime in which the algorithm settles to its optimized incarnation.