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.
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, 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.
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.
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An elementary proof of a theorem of Johnson and Lindenstrauss
Sanjoy Dasgupta;Anupam Gupta.
Random Structures and Algorithms (2003)
Near-optimal sensor placements: maximizing information while minimizing communication cost
Andreas Krause;Carlos Guestrin;Anupam Gupta;Jon Kleinberg.
information processing in sensor networks (2006)
Bounded geometries, fractals, and low-distortion embeddings
A. Gupta;R. Krauthgamer;J.R. Lee.
foundations of computer science (2003)
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)
Robust Submodular Observation Selection
Andreas Krause;H. Brendan McMahan;Carlos Guestrin;Anupam Gupta.
Journal of Machine Learning Research (2008)
Cuts, Trees and ℓ 1 -Embeddings of Graphs*
Anupam Gupta;Alistair Sinclair;Ilan Newman;Yuri Rabinovich.
foundations of computer science (1999)
Simpler and better approximation algorithms for network design
Anupam Gupta;Amit Kumar;Tim Roughgarden.
symposium on the theory of computing (2003)
Boosted sampling: approximation algorithms for stochastic optimization
Anupam Gupta;Martin Pál;R. Ravi;Amitabh Sinha.
symposium on the theory of computing (2004)
Constrained non-monotone submodular maximization: offline and secretary algorithms
Anupam Gupta;Aaron Roth;Grant Schoenebeck;Kunal Talwar.
workshop on internet and network economics (2010)
Approximate clustering without the approximation
Maria-Florina Balcan;Avrim Blum;Anupam Gupta.
symposium on discrete algorithms (2009)
Profile was last updated on December 6th, 2021.
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