Grants per year
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
2009 → 2012
Computer Science, MA, Princeton University
2007 → 2009
Computer Science and Engineering, BTech, Indian Institute of Technology, Madras
2003 → 2007
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Grants
- 4 Active
HDR TRIPODS: Collaborative Research: Institute for Data, Econometrics, Algorithms and Learning .
Hartline, J. D., Auerbach, E. J., Berry, R. A., Canay, I. A., Guo, D., Horowitz, J. L., Makarychev, K., Vijayaraghavan, A. & Wang, Z.
9/15/19 → 8/31/22
Project: Research project
CAREER: Beyond Worst-Case Analysis: New Approaches in Approximation Algorithms and Machine Learning
3/15/17 → 2/28/22
Project: Research project
PSC for CAREER: Beyond Worst-Case Analysis: New Approaches in Approximation Algorithms and Machine Learning
3/15/17 → 2/28/22
Project: Research project
AitF: Collaborative Research: Algorithms for Probabilistic Inference in the Real World
9/1/16 → 8/31/20
Project: Research project
Research Output
Smoothed Analysis in Unsupervised Learning via Decoupling
Bhaskara, A., Chen, A., Perreault, A. & Vijayaraghavan, A., Nov 2019, Proceedings - 2019 IEEE 60th Annual Symposium on Foundations of Computer Science, FOCS 2019. IEEE Computer Society, p. 582-610 29 p. 8948613. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; vol. 2019-November).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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), p. 469-494 26 p. (35th International Conference on Machine Learning, ICML 2018; vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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 2018, In : ACM Transactions on Algorithms. 14, 3, 26.Research output: Contribution to journal › Editorial
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 conference › Paper
4
Scopus
citations
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 proceeding › Conference contribution