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Murat A. Erdogdu
Murat A. Erdogdu
University of Toronto
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Seismic: A self-exciting point process model
Q Zhao, MA Erdogdu, HY He, A Rajaraman, J Leskovec
Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015
7332015
Convergence rates of sub-sampled Newton methods
MA Erdogdu, A Montanari
Advances in Neural Information Processing Systems, 1090-1098, 2015
1652015
Global non-convex optimization with discretized diffusions
MA Erdogdu, L Mackey, O Shamir
Advances in Neural Information Processing Systems 31, 2018
1132018
Convergence rates of active learning for maximum likelihood estimation
K Chaudhuri, SM Kakade, P Netrapalli, S Sanghavi
Advances in Neural Information Processing Systems 28, 2015
772015
Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
S Chewi, MA Erdogdu, MB Li, R Shen, M Zhang
arXiv preprint arXiv:2112.12662, 2021
752021
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
752019
Stochastic runge-kutta accelerates langevin monte carlo and beyond
X Li, Y Wu, L Mackey, MA Erdogdu
Advances in neural information processing systems 32, 2019
702019
Manipulating sgd with data ordering attacks
I Shumailov, Z Shumaylov, D Kazhdan, Y Zhao, N Papernot, MA Erdogdu, ...
Advances in Neural Information Processing Systems 34, 18021-18032, 2021
672021
Estimating lasso risk and noise level
M Bayati, MA Erdogdu, A Montanari
Advances in Neural Information Processing Systems 26, 2013
652013
High-dimensional asymptotics of feature learning: How one gradient step improves the representation
J Ba, MA Erdogdu, T Suzuki, Z Wang, D Wu, G Yang
Advances in Neural Information Processing Systems 35, 37932-37946, 2022
642022
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
592021
Hausdorff dimension, heavy tails, and generalization in neural networks
U Simsekli, O Sener, G Deligiannidis, MA Erdogdu
Advances in Neural Information Processing Systems, 2020
592020
Towards a theory of non-log-concave sampling: first-order stationarity guarantees for langevin monte carlo
K Balasubramanian, S Chewi, MA Erdogdu, A Salim, S Zhang
Conference on Learning Theory, 2896-2923, 2022
482022
Neural networks efficiently learn low-dimensional representations with sgd
A Mousavi-Hosseini, S Park, M Girotti, I Mitliagkas, MA Erdogdu
arXiv preprint arXiv:2209.14863, 2022
362022
Convergence rate of block-coordinate maximization Burer–Monteiro method for solving large SDPs
MA Erdogdu, A Ozdaglar, PA Parrilo, ND Vanli
Mathematical Programming 195 (1-2), 243-281, 2022
362022
An analysis of constant step size sgd in the non-convex regime: Asymptotic normality and bias
L Yu, K Balasubramanian, S Volgushev, MA Erdogdu
Advances in Neural Information Processing Systems, 2021
362021
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
362019
Robust estimation of neural signals in calcium imaging
H Inan, MA Erdogdu, M Schnitzer
Advances in neural information processing systems 30, 2017
352017
Convergence rates of stochastic gradient descent under infinite noise variance
H Wang, M Gurbuzbalaban, L Zhu, U Simsekli, MA Erdogdu
Advances in Neural Information Processing Systems 34, 18866-18877, 2021
342021
Flexible results for quadratic forms with applications to variance components estimation
LH Dicker, MA Erdogdu
Annals of Statistics 45 (1), 386-414, 2017
322017
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