Murat A. Erdogdu

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Andrea MontanariProfessor of Electrical Engineering and Statistics, Stanford UniversityVerified email at stanford.edu
Lee H. DickerRutgers University, Department of Statistics and BiostatisticsVerified email at stat.rutgers.edu
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Qingyuan ZhaoUniversity of CambridgeVerified email at statslab.cam.ac.uk
Anand RajaramanPartner, Rocketship.vcVerified email at cs.stanford.edu
Mohsen BayatiAssociate Professor, Stanford UniversityVerified email at stanford.edu
Umut SimsekliINRIA - École Normale SupérieureVerified email at inria.fr
Denny WuUniversity of TorontoVerified email at cs.toronto.edu
Lester MackeyMicrosoft ResearchVerified email at microsoft.com
Krishnakumar BalasubramanianUniversity of California, DavisVerified email at ucdavis.edu
George DeligiannidisAssociate Professor in Statistics, University of OxfordVerified email at stats.ox.ac.uk
Ozan SenerIntel LabsVerified email at cs.stanford.edu
Nadia FawazApplied Research Scientist, PinterestVerified email at pinterest.com
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Ohad ShamirWeizmann Institute of ScienceVerified email at weizmann.ac.il
Pablo A. ParriloProfessor of Electrical Engineering and Computer Science, Massachusetts Institute of TechnologyVerified email at mit.edu
Asuman OzdaglarEECS, MITVerified email at mit.edu
Nuri Denizcan VanliMassachusetts Institute of TechnologyVerified email at mit.edu
Xuechen LiPhD Student, Stanford UniversityVerified email at stanford.edu

Murat A. Erdogdu

Stanford University

Verified email at stanford.edu - Homepage

Optimization Statistics Machine Learning


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Seismic: A self-exciting point process model for predicting tweet popularity Q Zhao, MA Erdogdu, HY He, A Rajaraman, J Leskovec Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015	510	2015
Convergence rates of sub-sampled Newton methods MA Erdogdu, A Montanari Advances in Neural Information Processing Systems, 1090-1098, 2015	134	2015
Global non-convex optimization with discretized diffusions MA Erdogdu, L Mackey, O Shamir arXiv preprint arXiv:1810.12361, 2018	58	2018
Convergence rates of active learning for maximum likelihood estimation K Chaudhuri, SM Kakade, P Netrapalli, S Sanghavi Advances in Neural Information Processing Systems, 1090-1098, 2015	52	2015
Estimating lasso risk and noise level. M Bayati, MA Erdogdu, A Montanari NIPS 26, 944-952, 2013	49	2013
Stochastic runge-kutta accelerates langevin monte carlo and beyond X Li, D Wu, L Mackey, MA Erdogdu arXiv preprint arXiv:1906.07868, 2019	30	2019
Flexible results for quadratic forms with applications to variance components estimation LH Dicker, MA Erdogdu The Annals of Statistics 45 (1), 386-414, 2017	28	2017
Generalization of two-layer neural networks: An asymptotic viewpoint J Ba, M Erdogdu, T Suzuki, D Wu, T Zhang International conference on learning representations, 2019	27	2019
Maximum likelihood for variance estimation in high-dimensional linear models LH Dicker, MA Erdogdu Artificial Intelligence and Statistics, 159-167, 2016	20	2016
Accelerating SVRG via second-order information R Kolte, MA Erdogdu, A Ozgür Neural Information Processing Systems Workshop on Optimization for Machine …, 2015	20	2015
Scaled least squares estimator for glms in large-scale problems MA Erdogdu, LH Dicker, M Bayati Advances in Neural Information Processing Systems 29, 3324-3332, 2016	16	2016
Privacy-utility trade-off for time-series with application to smart-meter data MA Erdogdu, N Fawaz, A Montanari Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015	16	2015
Convergence rate of block-coordinate maximization Burer–Monteiro method for solving large SDPs MA Erdogdu, A Ozdaglar, PA Parrilo, ND Vanli Mathematical Programming, 1-39, 2021	15	2021
Robust Estimation of Neural Signals in Calcium Imaging. H Inan, MA Erdogdu, MJ Schnitzer NIPS, 2901-2910, 2017	15	2017
Privacy-utility trade-off under continual observation MA Erdogdu, N Fawaz 2015 IEEE International Symposium on Information Theory (ISIT), 1801-1805, 2015	15	2015
On the convergence of langevin monte carlo: The interplay between tail growth and smoothness MA Erdogdu, R Hosseinzadeh Conference on Learning Theory, 1776-1822, 2021	14	2021
Normal approximation for stochastic gradient descent via non-asymptotic rates of martingale CLT A Anastasiou, K Balasubramanian, MA Erdogdu Conference on Learning Theory, 115-137, 2019	14	2019
Newton-Stein method: A second order method for GLMs via Stein's lemma MA Erdogdu Advances in Neural Information Processing Systems 28, 1216-1224, 2015	14	2015
Hausdorff dimension, heavy tails, and generalization in neural networks U Şimşekli, O Sener, G Deligiannidis, MA Erdogdu arXiv preprint arXiv:2006.09313, 2020	13*	2020
Inference in graphical models via semidefinite programming hierarchies MA Erdogdu, Y Deshpande, A Montanari arXiv preprint arXiv:1709.06525, 2017	13	2017

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