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- Publications
- Influence

An elementary proof of a theorem of Johnson and Lindenstrauss

- S. Dasgupta, A. Gupta
- Computer Science, Mathematics
- Random Struct. Algorithms
- 2003

A result of Johnson and Lindenstrauss [13] shows that a set of n points in high dimensional Euclidean space can be mapped into an O(log n/e2)-dimensional Euclidean space such that the distance… Expand

Bounded geometries, fractals, and low-distortion embeddings

- A. Gupta, Robert Krauthgamer, J. Lee
- Mathematics, Computer Science
- 44th Annual IEEE Symposium on Foundations of…
- 11 October 2003

The doubling constant of a metric space (X, d) is the smallest value /spl lambda/ such that every ball in X can be covered by /spl lambda/ balls of half the radius. The doubling dimension of X is… Expand

Provisioning a virtual private network: a network design problem for multicommodity flow

- A. Gupta, J. Kleinberg, A. Kumar, R. Rastogi, B. Yener
- Computer Science
- STOC '01
- 6 July 2001

Consider a setting in which a group of nodes, situated in a large underlying network, wishes to reserve bandwidth on which to support communication. Virtual private networks (VPNs) are services that… Expand

Robust Submodular Observation Selection

- Andreas Krause, H. McMahan, Carlos Guestrin, A. Gupta
- Mathematics
- 2008

In many applications, one has to actively select among a set of expensive observations before making an informed decision. For example, in environmental monitoring, we want to select locations to… Expand

- 217
- 38
- Open Access

Approximate clustering without the approximation

- Maria-Florina Balcan, A. Blum, A. Gupta
- Computer Science
- SODA
- 4 January 2009

Approximation algorithms for clustering points in metric spaces is a flourishing area of research, with much research effort spent on getting a better understanding of the approximation guarantees… Expand

Simpler and better approximation algorithms for network design

- A. Gupta, A. Kumar, T. Roughgarden
- Computer Science
- STOC '03
- 9 June 2003

We give simple and easy-to-analyze randomized approximation algorithms for several well-studied NP-hard network design problems. Our algorithms improve over the previously best known approximation… Expand

Iterative Constructions and Private Data Release

In this paper we study the problem of approximately releasing the cut function of a graph while preserving differential privacy, and give new algorithms (and new analyses of existing algorithms) in… Expand

Boosted sampling: approximation algorithms for stochastic optimization

Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the Steiner Tree problem, for… Expand

Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms

- A. Gupta, A. Roth, Grant Schoenebeck, K. Talwar
- Computer Science, Mathematics
- WINE
- 7 March 2010

Constrained submodular maximization problems have long been studied, most recently in the context of auctions and computational advertising, with near-optimal results known under a variety of… Expand

When LP Is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings

- N. Bansal, A. Gupta, J. Li, Julián Mestre, V. Nagarajan, A. Rudra
- Computer Science, Mathematics
- Algorithmica
- 31 August 2010

Consider a random graph model where each possible edge e is present independently with some probability pe. Given these probabilities, we want to build a large/heavy matching in the randomly… Expand