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Selfimproving algorithms
 in SODA ’06: Proceedings of the seventeenth annual ACMSIAM symposium on Discrete algorithm
"... We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i) an al ..."
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Cited by 34 (6 self)
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We investigate ways in which an algorithm can improve its expected performance by finetuning itself automatically with respect to an arbitrary, unknown input distribution. We give such selfimproving algorithms for sorting and computing Delaunay triangulations. The highlights of this work: (i
Selfimproving Algorithms for Convex Hulls
, 2009
"... We give an algorithm for computing planar convex hulls in the selfimproving model: given a sequence I1, I2,... of planar npoint sets, the upper convex hull conv(I) of each set I is desired. We assume that there exists a probability distribution D on npoint sets, such that the inputs Ij are drawn ..."
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Cited by 2 (2 self)
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We give an algorithm for computing planar convex hulls in the selfimproving model: given a sequence I1, I2,... of planar npoint sets, the upper convex hull conv(I) of each set I is desired. We assume that there exists a probability distribution D on npoint sets, such that the inputs Ij are drawn
Selfimproving Algorithms for Coordinatewise Maxima [Extended Abstract]
"... Computing the coordinatewise maxima of a planar point set is a classic and wellstudied problem in computational geometry. We give an algorithm for this problem in the selfimproving setting. We have n (unknown) independent distributions D1, D2,..., Dn of planar points. An input pointset (p1, p2,... ..."
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Cited by 1 (0 self)
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,..., pn) is generated by taking an independent sample pi from each Di, so the input distribution D is the product i Di. A selfimproving algorithm repeatedly gets input sets from the distribution D (which is a priori unknown) and tries to optimize its running time for D. Our algorithm uses the first few
CS369N: Beyond WorstCase Analysis Lecture #5: SelfImproving Algorithms ∗
, 2010
"... Last lecture concluded with a discussion of semirandom graph models, an interpolation between worstcase analysis and averagecase analysis designed to identify robust algorithms in the face of strong impossibility results for worstcase guarantees. This lecture and the next two give three more ana ..."
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analysis frameworks that blend aspects of worst and averagecase analysis. Today’s model, of selfimproving algorithms, is the closest to traditional averagecase analysis. The model and results are by Ailon, Chazelle, Comandar, and Liu [1]. The Setup. For a given computational problem, we posit a
Selfimproving reactive agents based on reinforcement learning, planning and teaching
 Machine Learning
, 1992
"... Abstract. To date, reinforcement learning has mostly been studied solving simple learning tasks. Reinforcement learning methods that have been studied so far typically converge slowly. The purpose of this work is thus twofold: 1) to investigate the utility of reinforcement learning in solving much ..."
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Cited by 314 (3 self)
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was used as a testbed. The enviromaaent is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Fast Algorithms for Mining Association Rules
, 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
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Cited by 3551 (15 self)
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We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known
Algorithms for Scalable Synchronization on SharedMemory Multiprocessors
 ACM Transactions on Computer Systems
, 1991
"... Busywait techniques are heavily used for mutual exclusion and barrier synchronization in sharedmemory parallel programs. Unfortunately, typical implementations of busywaiting tend to produce large amounts of memory and interconnect contention, introducing performance bottlenecks that become marke ..."
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Cited by 567 (32 self)
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markedly more pronounced as applications scale. We argue that this problem is not fundamental, and that one can in fact construct busywait synchronization algorithms that induce no memory or interconnect contention. The key to these algorithms is for every processor to spin on separate locally
The Macroscopic Behavior of the TCP Congestion Avoidance Algorithm
, 1997
"... In this paper, we analyze a performance model for the TCP Congestion Avoidance algorithm. The model predicts the bandwidth of a sustained TCP connection subjected to light to moderate packet losses, such as loss caused by network congestion. It assumes that TCP avoids retransmission timeouts and alw ..."
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Cited by 648 (18 self)
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In this paper, we analyze a performance model for the TCP Congestion Avoidance algorithm. The model predicts the bandwidth of a sustained TCP connection subjected to light to moderate packet losses, such as loss caused by network congestion. It assumes that TCP avoids retransmission timeouts
Results 1  10
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