Srinadh Bhojanapalli - Google Scholar Citations

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	All	Since 2014
Citations	1236	1232
h-index	16	16
i10-index	16	16

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20142015201620172018201912 74 130 165 347 491

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Behnam NeyshaburSr. Research Scientist, GoogleVerified email at google.com
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Sujay SanghaviAssociate Professor, Electrical and Computer Engineering, WNCG, University of Texas, AustinVerified email at mail.utexas.edu
Anastasios KyrillidisAssistant Professor, Computer Science Department, Rice UniversityVerified email at rice.edu
Rachel WardUT AustinVerified email at math.utexas.edu
Yudong ChenAssistant Professor, Cornell UniversityVerified email at cornell.edu
Prateek JainMicrosoft Research IndiaVerified email at microsoft.com
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.edu
Zhiyuan LiGraduate Student, Princeton UniversityVerified email at princeton.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Dohyung ParkThe University of Texas at AustinVerified email at utexas.edu
Ayan ChakrabartiAssistant Professor of Computer Science & Engineering, Washington University in St. LouisVerified email at wustl.edu
Praneeth NetrapalliMicrosoft ResearchVerified email at microsoft.com
Nicolas BoumalPrinceton UniversityVerified email at math.princeton.edu
Amir KalevJoint Center for Quantum Information and Computer Science (QuICS)Verified email at umd.edu
Alexandros G DimakisAssociate Professor, ECE University of Texas at AustinVerified email at austin.utexas.edu
David McAllesterProfessor, Toyota Technological Institute at ChicagoVerified email at ttic.edu

Srinadh Bhojanapalli

Research Scientist, Google Research

Verified email at google.com - Homepage

Machine learning Algorithms


Title	Cited by	Year
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in Neural Information Processing Systems, 5947-5956, 2017	231	2017
Global optimality of local search for low rank matrix recovery S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 3873-3881, 2016	205	2016
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks B Neyshabur, S Bhojanapalli, N Srebro arXiv preprint arXiv:1707.09564, 2017	145	2017
Coherent matrix completion. Y Chen, S Bhojanapalli, S Sanghavi, R Ward arXiv preprint arXiv:1306.2979, 2013	110	2013
Dropping convexity for faster semi-definite optimization S Bhojanapalli, A Kyrillidis, S Sanghavi Conference on Learning Theory, 530-582, 2016	106	2016
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 6151-6159, 2017	73	2017
Universal matrix completion S Bhojanapalli, P Jain arXiv preprint arXiv:1402.2324, 2014	73	2014
Towards understanding the role of over-parametrization in generalization of neural networks B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro arXiv preprint arXiv:1805.12076, 2018	60	2018
Completing any low-rank matrix, provably Y Chen, S Bhojanapalli, S Sanghavi, R Ward The Journal of Machine Learning Research 16 (1), 2999-3034, 2015	58	2015
Tighter low-rank approximation via sampling the leveraged element S Bhojanapalli, P Jain, S Sanghavi Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2015	36	2015
A new sampling technique for tensors S Bhojanapalli, S Sanghavi arXiv preprint arXiv:1502.05023, 2015	28	2015
Stabilizing gan training with multiple random projections B Neyshabur, S Bhojanapalli, A Chakrabarti arXiv preprint arXiv:1705.07831, 2017	22	2017
The role of over-parametrization in generalization of neural networks B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro	19	2018
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form S Bhojanapalli, N Boumal, P Jain, P Netrapalli arXiv preprint arXiv:1803.00186, 2018	19	2018
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems D Park, A Kyrillidis, S Bhojanapalli, C Caramanis, S Sanghavi arXiv preprint arXiv:1606.01316, 2016	19	2016
Provable non-convex projected gradient descent for a class of constrained matrix optimization problems D Park, A Kyrillidis, S Bhojanapalli, C Caramanis, S Sanghavi arXiv preprint arXiv:1606.01316, 2016	16	2016
Single pass PCA of matrix products S Wu, S Bhojanapalli, S Sanghavi, AG Dimakis Advances in Neural Information Processing Systems, 2585-2593, 2016	6	2016
Provable quantum state tomography via non-convex methods A Kyrillidis, A Kalev, D Park, S Bhojanapalli, C Caramanis, S Sanghavi arXiv preprint arXiv:1711.02524, 2017	5	2017
Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962 1 (5), 2019	3	2019
Provable compressed sensing quantum state tomography via non-convex methods A Kyrillidis, A Kalev, D Park, S Bhojanapalli, C Caramanis, S Sanghavi npj Quantum Information 4 (1), 36, 2018	2	2018

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