Praneeth Netrapalli

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Prateek JainGoogle Research IndiaDirección de correo verificada de google.com
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Chi JinAssistant Professor, Princeton UniversityDirección de correo verificada de princeton.edu
Sujay SanghaviAssociate Professor, Electrical and Computer Engineering, University of Texas, AustinDirección de correo verificada de mail.utexas.edu
Aaron SidfordStanford UniversityDirección de correo verificada de stanford.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Rong GeDuke UniversityDirección de correo verificada de cs.duke.edu
Dheeraj NagarajResearch Scientist, GoogleDirección de correo verificada de google.com
Sewoong OhPaul G. Allen School of Computer Science and Engineering, University of WashingtonDirección de correo verificada de cs.washington.edu
Kiran Koshy ThekumparampilAmazon, University of Illinois at Urbana-ChampaignDirección de correo verificada de illinois.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIADirección de correo verificada de caltech.edu
Alekh AgarwalGoogleDirección de correo verificada de google.com
Aravind RajeswaranMeta AI, UC BerkeleyDirección de correo verificada de cs.washington.edu
Thorsten JoachimsProfessor of Computer Science, Cornell UniversityDirección de correo verificada de cs.cornell.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Joe NeemanStatistics, UC BerkeleyDirección de correo verificada de stat.berkeley.edu
Dan GarberAssociate Professor at Technion - Israel Institute of TechnologyDirección de correo verificada de technion.ac.il
Rashish TandonAppleDirección de correo verificada de apple.com
Niranjan Uma NareshDirector of Science and EngineeringDirección de correo verificada de uci.edu

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Praneeth Netrapalli

Google Research

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Machine learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Low-rank matrix completion using alternating minimization P Jain, P Netrapalli, S Sanghavi Proceedings of the forty-fifth annual ACM symposium on Theory of computing …, 2013	1271	2013
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017	1051	2017
Morel: Model-based offline reinforcement learning R Kidambi, A Rajeswaran, P Netrapalli, T Joachims Advances in neural information processing systems 33, 21810-21823, 2020	816	2020
Phase retrieval using alternating minimization P Netrapalli, P Jain, S Sanghavi Advances in Neural Information Processing Systems 26, 2013	749	2013
What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020	489*	2020
The pitfalls of simplicity bias in neural networks H Shah, K Tamuly, A Raghunathan, P Jain, P Netrapalli Advances in Neural Information Processing Systems 33, 9573-9585, 2020	425	2020
Non-convex robust PCA P Netrapalli, N UN, S Sanghavi, A Anandkumar, P Jain Advances in neural information processing systems 27, 2014	377	2014
Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018	300	2018
On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan Journal of the ACM (JACM) 68 (2), 1-29, 2021	256*	2021
Efficient algorithms for smooth minimax optimization KK Thekumparampil, P Jain, P Netrapalli, S Oh Advances in neural information processing systems 32, 2019	237	2019
Efficient domain generalization via common-specific low-rank decomposition V Piratla, P Netrapalli, S Sarawagi International conference on machine learning, 7728-7738, 2020	236	2020
Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Journal of machine learning research 18 (223), 1-42, 2018	230*	2018
Learning the graph of epidemic cascades P Netrapalli, S Sanghavi ACM SIGMETRICS Performance Evaluation Review 40 (1), 211-222, 2012	222	2012
The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares R Ge, SM Kakade, R Kidambi, P Netrapalli Advances in neural information processing systems 32, 2019	203	2019
Learning sparsely used overcomplete dictionaries via alternating minimization A Agarwal, A Anandkumar, P Jain, P Netrapalli SIAM Journal on Optimization 26 (4), 2775-2799, 2016	203	2016
A short note on concentration inequalities for random vectors with subgaussian norm C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan arXiv preprint arXiv:1902.03736, 2019	177	2019
Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for oja’s algorithm P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford Conference on learning theory, 1147-1164, 2016	160	2016
Information-theoretic thresholds for community detection in sparse networks J Banks, C Moore, J Neeman, P Netrapalli Conference on Learning Theory, 383-416, 2016	159*	2016
Accelerating stochastic gradient descent for least squares regression P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford Conference On Learning Theory, 545-604, 2018	151	2018
On the insufficiency of existing momentum schemes for stochastic optimization R Kidambi, P Netrapalli, P Jain, S Kakade 2018 Information Theory and Applications Workshop (ITA), 1-9, 2018	145	2018

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