• Publications
  • Influence
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
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Approximate clustering via core-sets
TLDR
In this paper, we show that for several clustering problems one can extract a small set of points, so that using those core-sets enable us to perform approximate clustering efficiently. Expand
  • 410
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Approximating extent measures of points
TLDR
We present a general ϵ-approximation algorithm for approximating various descriptors of the extent of a set <i>P</i> of points in R<sup><i>d</i></sup> with the property that (1 − ϵ)μ(<i> P</i>) ≤ μ(< i>Q</i>. Expand
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Fast construction of nets in low dimensional metrics, and their applications
TLDR
We present a near linear time algorithm for constructing hierarchical nets in finite metric spaces with constant doubling dimension. Expand
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Generalization Bounds for the Area Under the ROC Curve
TLDR
We study generalization properties of the area under the ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for the bipartite ranking problem. Expand
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On coresets for k-means and k-median clustering
TLDR
In this paper, we show the existence of small coresets for the problems of computing k-median and k-means clustering for points in low dimension. Expand
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Geometric Approximation via Coresets
The paradigm of coresets has recently emerged as a powerful tool for efficiently approximating various extent measures of a point set P . Using this paradigm, one quickly computes a small subset Q ofExpand
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Random Walks
  • Sariel Har-Peled
  • Computer Science
  • Encyclopedia of Social Network Analysis and…
  • 2014
TLDR
The average number of visits to a > 0 before returning to the origin is 1 (hint: show that it is closely related to the expectation of some geometric random variable). Expand
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On approximating the depth and related problems
TLDR
We study the problem of finding a disk covering the largest number of red points, while avoiding all the blue points, and provide a near-linear expected-time randomized approximation algorithm for this problem. Expand
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Efficiently approximating the minimum-volume bounding box of a point set in three dimensions
TLDR
We present an efficient O(n+1/?4.5-time) algorithm for computing a (1+?)-approximation of the minimum-volume bounding box of n points in R3. Expand
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