Publications
Journal / preprints
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
preprint , [Arxiv].
Stabilizing GAN Training with Multiple Random Projections
Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti
preprint , [Arxiv], [Project website].
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi
preprint , [Arxiv].
A New Sampling Technique for Tensors
Srinadh Bhojanapalli, Sujay Sanghavi
preprint, [Arxiv], [slides].
Completing any Low-rank Matrix, Provably
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward
Journal of Machine Learning Research (JMLR) 2015
[Arxiv], [JMLR].
Conference
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
Conference on Learning Theory (COLT) 2018
[Arxiv].
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
International Conference on Learning Representations (ICLR) 2018
[Arxiv].
Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
Neural Information Processing Systems (NIPS) 2017
[Arxiv].
Implicit Regularization in Matrix Factorization
Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Neural Information Processing Systems (NIPS) 2017
[Arxiv], [slides].
Single Pass PCA of Matrix Products
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alex Dimakis
Neural Information Processing Systems (NIPS) 2016
[Arxiv], [SPARK code].
Global Optimality of Local Search for Low Rank Matrix Recovery
Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Neural Information Processing Systems (NIPS) 2016
[Arxiv].
Dropping Convexity for Faster Semi-definite Optimization
Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi
Conference on Learning Theory (COLT) 2016
[Arxiv], [COLT], [slides].
Tighter Low-rank Approximation via Sampling the Leveraged Element
Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi
ACM-SIAM Symposium On Discrete Algorithms (SODA) 2015
[Arxiv], [SODA], [slides].
Universal Matrix Completion
Srinadh Bhojanapalli, Prateek Jain
International Conference on Machine Learning (ICML) 2014
[Arxiv], [ICML], [slides], [video].
Coherent Matrix Completion
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward
International Conference on Machine Learning (ICML) 2014
[Arxiv], [ICML], [slides], [video].
PhD Thesis
Large Scale Matrix Factorization with Guarantees: Sampling and Bi-linearity [pdf]
UT Austin, 2015.
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