Murat A. Erdogdu
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University of Toronto
· Department of Computer Science
· Department of Statistical Sciences
Vector Institute
Contact
Pratt 286b, 6 King’s College Rd.
Toronto, ON M5S 3G4
erdogdu at cs.toronto dot edu
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I am an assistant professor at the University of Toronto in departments of Computer Science and Statistical Sciences. I am a faculty member of the Machine Learning Group and the Vector Institute, and a CIFAR Chair in Artificial Intelligence.
Before, I was a postdoctoral researcher at
Microsoft Research - New England.
I did my Ph.D. at
Department of Statistics at Stanford University
where I was jointly advised by Mohsen Bayati and Andrea Montanari.
I have an M.S. degree in Computer Science from Stanford,
and B.S. degrees in Electrical Engineering and Mathematics,
both from Bogazici University.
Research Interests
Machine Learning: Theory for learning and sampling algorithms
Optimization: Non-convex, convex algorithms for machine learning
Statistics: High-dimensional data analysis, regularization and shrinkage
Some Recent Papers
L. Yu, K. Balasubramanian, S. Volgushev and M.A. Erdogdu,
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias, 2020
M.A. Erdogdu and R. Hosseinzadeh,
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness, 2020
J. Ba, M.A. Erdogdu, T. Suzuki, D. Wu and T. Zhang,
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint, 2020
X. Li, D. Wu, L. Mackey and M.A. Erdogdu,
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond, 2019
A. Anastasiou, K. Balasubramanian and M.A. Erdogdu,
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT, 2019
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