Publications

* indicates alphabetical ordering of author names.

Surveys

  1. Stochastic Gradient Descent and Its Variants in Machine Learning (Full article)
    P. Netrapalli
    Journal of the Indian Institute of Science

Research articles

  1. Efficient Algorithms for Smooth Minimax Optimization
    K. K. Thekumparampil, P. Jain, P. Netrapalli and S. Oh
    Manuscript

  2. The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure [*]
    R. Ge, S. M. Kakade, R. Kidambi and P. Netrapalli
    Manuscript

  3. Making the Last Iterate of SGD Information Theoretically Optimal [*]
    P. Jain, D. Nagaraj and P. Netrapalli
    COLT 2019

  4. SGD without Replacement: Sharper Rates for General Smooth Convex Functions [*]
    P. Jain, D. Nagaraj and P. Netrapalli
    ICML 2019

  5. Online Non-Convex Learning: Following the Perturbed Leader is Optimal
    A. S. Suggala and P. Netrapalli
    Manuscript

  6. What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
    C. Jin, P. Netrapalli and M. I. Jordan
    Manuscript

  7. Stochastic Gradient Descent Escapes Saddle Points Efficiently
    C. Jin, P. Netrapalli, R. Ge, S. M. Kakade and M. I. Jordan
    Manuscript

  8. A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
    C. Jin, P. Netrapalli, R. Ge, S. M. Kakade and M. I. Jordan
    Manuscript

  9. Support Recovery for Orthogonal Matching Pursuit: Upper and Lower Bounds
    R. Somani, C. Gupta, P. Jain and P. Netrapalli
    NeurIPS 2018 (Spotlight)

  10. Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form [*]
    S. Bhojanapalli, N. Boumal, P. Jain and P. Netrapalli
    COLT 2018
    See Nicolas’ and Srinadh's interview about this paper on Dustin Mixon's blog

  11. On the insufficiency of existing momentum schemes for Stochastic Optimization
    R. Kidambi, P. Netrapalli, P. Jain and S. M. Kakade
    ICLR 2018 (Oral)

  12. Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
    C. Jin, P. Netrapalli and M. I. Jordan
    COLT 2018

  13. Leverage Score Sampling for Faster Accelerated Regression and ERM [*]
    N. Agarwal, S. M. Kakade, R. Kidambi, Y. T. Lee, P. Netrapalli and A. Sidford
    Manuscript

  14. Accelerating Stochastic Gradient Descent For Least Squares Regression [*]
    P. Jain, S. M. Kakade, R. Kidambi, P. Netrapalli and A. Sidford
    COLT 2018

  15. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness [*]
    C. Musco, P. Netrapalli, A. Sidford, S. Ubaru and D. P. Woodruff
    ITCS 2018

  16. A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares) [*]
    P. Jain, S. M. Kakade, R. Kidambi, P. Netrapalli, V. K. Pillutla and A. Sidford
    FSTTCS 2017 (Invited)

  17. How to Escape Saddle Points Efficiently
    C. Jin, R. Ge, P. Netrapalli, S. M. Kakade and M. I. Jordan
    ICML 2017

  18. Thresholding based Efficient Outlier Robust PCA [*]
    Y. Cherapanamjeri, P. Jain and P. Netrapalli
    COLT 2017

  19. Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification [*]
    P. Jain, S. M. Kakade, R. Kidambi, P. Netrapalli and A. Sidford
    Journal of Machine Learning Research (JMLR) 18(223)

  20. Computing Matrix Squareroot via Non Convex Local Search [*]
    P. Jain, C. Jin, S. M. Kakade and P. Netrapalli
    AISTATS 2017

  21. Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
    C. Jin, S. M. Kakade and P. Netrapalli
    NIPS 2016

  22. Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm [*]
    P. Jain, C. Jin, S. M. Kakade, P. Netrapalli and A. Sidford
    COLT 2016

  23. Information-theoretic thresholds for community detection in sparse networks [*]
    J. Banks, C. Moore, J. Neeman and P. Netrapalli
    COLT 2016

    This paper was a merge of the following two papers
    Non-Reconstructability in the Stochastic Block Model [*]    J. Neeman and P. Netrapalli
    and
    Information-theoretic thresholds for community detection in sparse networks    J. Banks and C. Moore

  24. Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis [*]
    R. Ge, C. Jin, S. M. Kakade, P. Netrapalli and A. Sidford
    ICML 2016

  25. Faster Eigenvector Computation via Shift-and-Invert Preconditioning [*]
    D. Garber, E. Hazan, C. Jin, S. M. Kakade, C. Musco, P. Netrapalli and A. Sidford
    ICML 2016

    This paper was a merge of the following two papers
    Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation [*]
    C. Jin, S. M. Kakade, C. Musco, P. Netrapalli and A. Sidford
    and
    Fast and Simple PCA via Convex Optimization    D. Garber and E. Hazan

  26. Learning Planar Ising Models
    J. K. Johnson, D. Oyen, M. Chertkov and P. Netrapalli
    Journal of Machine Learning Research (JMLR) 2016, Volume 17, Issue 15

  27. Convergence Rates of Active Learning for Maximum Likelihood Estimation [*]
    K. Chaudhuri, S. M. Kakade, P. Netrapalli and S. Sanghavi
    NIPS 2015

  28. Fast Exact Matrix Completion with Finite Samples [*]
    P. Jain and P. Netrapalli
    COLT 2015

  29. Non-convex Robust PCA
    P. Netrapalli, U. N. Niranjan, S. Sanghavi, A. Anandkumar and P. Jain
    NIPS 2014 (Spotlight)

  30. Learning Structure of Power-Law Markov Networks
    A. K. Das, P. Netrapalli, S. Sanghavi and S. Vishwanath
    ISIT 2014

  31. Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization [*]
    A. Agarwal, A. Anandkumar, P. Jain and P. Netrapalli
    SIAM Journal on Optimization 2016, Vol. 26, Issue 4, Pages 2775–2799

  32. A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries [*]
    A. Agarwal, A. Anandkumar and P. Netrapalli
    IEEE Transactions on Information Theory 2017, Vol. 63, Issue 1, Pages 575–592

    An extended abstract of the above two papers appeared as
    Learning Sparsely Used Overcomplete Dictionaries [*]
    A. Agarwal, A. Anandkumar, P. Jain, P. Netrapalli and R. Tandon
    COLT 2014

    See also this paper by Arora, Ge and Moitra.

  33. Phase Retrieval using Alternating Minimization
    P. Netrapalli, P. Jain and S. Sanghavi
    IEEE Transactions on Signal Processing 2015, Vol. 63, Issue 18, Pages 4814–4826
    An extended abstract appeared in NIPS 2013

  34. One-Bit Compressed Sensing: Provable Support and Vector Recovery
    S. Gopi, P. Netrapalli, P. Jain and A. Nori
    ICML 2013

  35. Low-rank Matrix Completion using Alternating Minimization [*]
    P. Jain, P. Netrapalli and S. Sanghavi
    STOC 2013

  36. Learning Markov Graphs Up To Edit Distance
    A. K. Das, P. Netrapalli, S. Sanghavi and S. Vishwanath
    ISIT 2012

  37. Finding the Graph of Epidemic Cascades
    P. Netrapalli and S. Sanghavi
    SIGMETRICS/Performance 2012

  38. Greedy Learning of Markov Network Structure
    P. Netrapalli, S. Banerjee, S. Sanghavi and S. Shakkottai
    Allerton 2010 (Invited)