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Personal profile

Research Interests

His research interests are broadly in the field of Theoretical Computer Science, particularly, in designing efficient algorithms for problems in Combinatorial Optimization and Machine Learning. He is also interested in using paradigms that go Beyond Worst-Case Analysis to obtain good algorithmic guarantees.

Education/Academic qualification

Computer Science, PhD, Princeton University

20092012

Computer Science, MA, Princeton University

20072009

Computer Science and Engineering, BTech, Indian Institute of Technology, Madras

20032007

Fingerprint Dive into the research topics where Aravindan Vijayaraghavan is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Approximation algorithms Engineering & Materials Science
Approximation Algorithms Mathematics
Polynomials Engineering & Materials Science
Tensors Engineering & Materials Science
Tensor Decomposition Mathematics
Hardness Mathematics
Decomposition Engineering & Materials Science
Max-cut Mathematics

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Grants 2016 2022

Learning systems
Operations research
Unsupervised learning
Engineering research
Electrical engineering
Approximation algorithms
Learning systems
Combinatorial optimization
Computer science
Students
Learning systems
Combinatorial optimization
Computer science
Students
Unsupervised learning
Learning systems
Combinatorial optimization
Computer vision
Structural properties
Approximation algorithms

Research Output 2010 2018

Clustering semi-random mixtures of Gaussians

Awasthi, P. & Vijayaraghavan, A., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 1. p. 469-494 26 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Clustering algorithms
Polynomials
Statistical Models

Editorial: ACM-SIAM symposium on discrete algorithms (SODA) 2016 special issue

Bhattacharyya, A., Grandoni, F., Nikolov, A., Saha, B., Saurabh, S., Vijayaraghavan, A. & Zhang, Q., Jul 1 2018, In : ACM Transactions on Algorithms. 14, 3, 26.

Research output: Contribution to journalEditorial

Optimality of approximate inference algorithms on stable instances

Lang, H., Sontag, D. & Vijayaraghavan, A., Jan 1 2018, p. 1157-1166. 10 p.

Research output: Contribution to conferencePaper

Optimality
LP Relaxation
Performance Guarantee
Approximate Algorithm
Potts model

Towards learning sparsely used dictionaries with arbitrary supports

Awasthi, P. & Vijayaraghavan, A., Nov 30 2018, Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018. Thorup, M. (ed.). IEEE Computer Society, p. 283-296 14 p. 8555113. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Glossaries
Polynomials
Byproducts

Approximation algorithms for label cover and the log-density threshold

Chlamtáč, E., Manurangsi, P., Moshkovitz, D. & Vijayaraghavan, A., Jan 1 2017, 28th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017. Klein, P. N. (ed.). Association for Computing Machinery, p. 900-919 20 p. (Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Approximation algorithms
Labels
Approximation Algorithms
Projection
Cover