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- Anupam Gupta

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Computer Science
H-index
58
Citations
10,500
181
World Ranking
1770
National Ranking
991

- Computer network
- Combinatorics
- Algorithm

Anupam Gupta mainly investigates Combinatorics, Approximation algorithm, Discrete mathematics, Mathematical optimization and Algorithm. His work in the fields of Binary logarithm overlaps with other areas such as Monotone polygon. He has included themes like Computational complexity theory, Wireless sensor network, Minimax approximation algorithm, Probabilistic logic and Optimization problem in his Approximation algorithm study.

His work deals with themes such as Embedding and Greedy algorithm, which intersect with Discrete mathematics. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Network planning and design, CURE data clustering algorithm and Cluster analysis. His Algorithm study incorporates themes from Submodular set function, Open problem and Theoretical computer science.

- An elementary proof of a theorem of Johnson and Lindenstrauss (719 citations)
- Near-optimal sensor placements: maximizing information while minimizing communication cost (418 citations)
- Bounded geometries, fractals, and low-distortion embeddings (410 citations)

Anupam Gupta mostly deals with Combinatorics, Approximation algorithm, Discrete mathematics, Mathematical optimization and Algorithm. His research integrates issues of Embedding, Bounded function and Metric space in his study of Combinatorics. His Embedding research is multidisciplinary, incorporating elements of Distortion and Euclidean space.

His work in Approximation algorithm addresses issues such as Steiner tree problem, which are connected to fields such as Covering problems and Combinatorial optimization. His biological study spans a wide range of topics, including Linear programming relaxation and Metric. The Mathematical optimization study combines topics in areas such as Scheduling, Graph and Competitive analysis.

- Combinatorics (45.45%)
- Approximation algorithm (37.66%)
- Discrete mathematics (27.60%)

- Combinatorics (45.45%)
- Algorithm (15.91%)
- Competitive analysis (11.04%)

Combinatorics, Algorithm, Competitive analysis, Mathematical optimization and Online algorithm are his primary areas of study. His Combinatorics research includes themes of Bounded function and Metric space. His work on Randomized algorithm as part of his general Algorithm study is frequently connected to Corruption, thereby bridging the divide between different branches of science.

His studies in Competitive analysis integrate themes in fields like Binary logarithm and Submodular set function. Anupam Gupta studies Approximation algorithm which is a part of Mathematical optimization. His studies deal with areas such as Constraint, Markov chain and Cache as well as Approximation algorithm.

- Better Algorithms for Stochastic Bandits with Adversarial Corruptions (36 citations)
- Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks. (18 citations)
- Chasing convex bodies with linear competitive ratio (15 citations)

- Computer network
- Algorithm
- Statistics

His primary areas of investigation include Combinatorics, Algorithm, Competitive analysis, Online algorithm and Treewidth. His research integrates issues of Bounded function, Metric space and Cluster analysis in his study of Combinatorics. Anupam Gupta has researched Bounded function in several fields, including Vertex, Induced subgraph, Graph partition, Approximation algorithm and Minimax approximation algorithm.

His studies in Algorithm integrate themes in fields like Adversarial system, Regret, Convolutional neural network and Scale invariance. His study in Competitive analysis is interdisciplinary in nature, drawing from both Tree and Matching. His Treewidth research includes elements of Submodular set function, Set cover problem and Linear programming relaxation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

An elementary proof of a theorem of Johnson and Lindenstrauss

Sanjoy Dasgupta;Anupam Gupta.

Random Structures and Algorithms **(2003)**

969 Citations

Near-optimal sensor placements: maximizing information while minimizing communication cost

Andreas Krause;Carlos Guestrin;Anupam Gupta;Jon Kleinberg.

information processing in sensor networks **(2006)**

554 Citations

Bounded geometries, fractals, and low-distortion embeddings

A. Gupta;R. Krauthgamer;J.R. Lee.

foundations of computer science **(2003)**

439 Citations

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

Anupam Gupta;Jon Kleinberg;Amit Kumar;Rajeev Rastogi.

symposium on the theory of computing **(2001)**

359 Citations

Robust Submodular Observation Selection

Andreas Krause;H. Brendan McMahan;Carlos Guestrin;Anupam Gupta.

Journal of Machine Learning Research **(2008)**

288 Citations

Cuts, Trees and ℓ 1 -Embeddings of Graphs*

Anupam Gupta;Alistair Sinclair;Ilan Newman;Yuri Rabinovich.

foundations of computer science **(1999)**

234 Citations

Simpler and better approximation algorithms for network design

Anupam Gupta;Amit Kumar;Tim Roughgarden.

symposium on the theory of computing **(2003)**

212 Citations

Boosted sampling: approximation algorithms for stochastic optimization

Anupam Gupta;Martin Pál;R. Ravi;Amitabh Sinha.

symposium on the theory of computing **(2004)**

195 Citations

Constrained non-monotone submodular maximization: offline and secretary algorithms

Anupam Gupta;Aaron Roth;Grant Schoenebeck;Kunal Talwar.

workshop on internet and network economics **(2010)**

164 Citations

Approximate clustering without the approximation

Maria-Florina Balcan;Avrim Blum;Anupam Gupta.

symposium on discrete algorithms **(2009)**

163 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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