(2016a). Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint,  2020, X. Li, D. Wu, L. Mackey and M.A. View Murat A. Erdogdu’s profile on LinkedIn, the world’s largest professional community. ∙ Erdogdu, T. Suzuki, D. Wu and T. Zhang, Before, I was a postdoctoral researcher at ill... 387 Followers, 612 Following, 78 Posts - See Instagram photos and videos from Murat Erdogdu (@merdogdu29_official) An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias,  2020, M.A. Articles Cited by Co-authors. ∙ gotz finds Götz More tips Academic Employment. disc... Rates of Martingale CLT, Global Non-convex Optimization with Discretized Diffusions, Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method Department of Statistical Sciences 9th Floor, Ontario Power Building 700 University Ave., Toronto, ON M5G 1Z5; 416-978-3452; Email Us 06/16/2020 ∙ by Umut Şimşekli, et al. 0 ∙ Stanford University (9) Microsoft Research (2) Stanford Graduate School of Business (2) Howard Hughes Medical Institute (1) Contact. 82, Claim your profile and join one of the world's largest A.I. 09/19/2017 ∙ by Murat A. Erdogdu, et al. both from Bogazici University. Jan 13. 0 Show Academic Trajectory 107, Deep Convolutional Neural Networks with Unitary Weights, 02/23/2021 ∙ by Hao-Yuan Chang ∙ Stein's Lemma and Subsampling in Large-Scale Optimization. Assistant Professor of Computer Science at University of Toronto, 2018-Current. Growth and Smoothness, Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond, Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic ∙ Announcements •Homework 2 is released, due on Feb 22. Year; Seismic: A self-exciting point process model for predicting tweet popularity. Stanford University. Microsoft Research - New England. In stochastic optimization, the population risk is generally approximate... We consider the problem of efficiently computing the maximum likelihood 10/29/2018 ∙ by Murat A. Erdogdu, et al. 1 Murat A. Erdogdu 4 Papers; Scaled Least Squares Estimator for GLMs in Large-Scale Problems (2016) Convergence rates of sub-sampled Newton methods (2015) Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma (2015) Estimating LASSO Risk and Noise Level (2013) Neural Information Processing Systems (NIPS) 0 Murat A. Erdogdu & David Duvenaud Department of Computer Science Department of Statistical Sciences Lecture 4 STA414/2104 Statistical Methods for Machine Learning II Slide credits: Russ Salakhutdinov 1. Murat Erdogdu Controlling & Finance and Reporting - Transaction Support Manager at Siemens München, Bayern, Deutschland Finanzdienstleistungen Department of Statistical Sciences 9th Floor, Ontario Power Building 700 University Ave., Toronto, ON M5G 1Z5; 416-978-3452; Email Us I am a faculty member of the Machine Learning Group and the Vector Institute, and a CIFAR Chair in Artificial Intelligence. Applied Filters. 11/28/2015 ∙ by Murat A. Erdogdu, et al. Murat ERDOGDU adlı kullanıcının LinkedIn‘deki tam profili görün ve bağlantılarını ve benzer şirketlerdeki iş ilanlarını keşfedin. share, We provide non-asymptotic convergence rates of the Polyak-Ruppert averag... ∙ share, Maximum A posteriori Probability (MAP) inference in graphical models amo... Department of … Assistant Professor of Statistics at University of Toronto, 2018-Current. Generalization in Neural Networks, An Analysis of Constant Step Size SGD in the Non-convex Regime: share, We consider the problem of efficiently computing the maximum likelihood 04/03/2019 ∙ by Andreas Anastasiou, et al. Toronto, ON M5S 3G4 erdogdu at cs.toronto dot edu. Pratt 286b, 6 King’s College Rd. Erdogdu, Cited by. Check out what Murat A. Erdogdu will be attending at NIPS 2013 See what Murat A. Erdogdu will be attending and learn more about the event taking place Dec 4 - 10, 2013 in Lake Tahoe, Nevada. Verified email at stanford.edu - Homepage. View Murat A. Erdogdu’s profile on LinkedIn, the world's largest professional community. –You should have received your crowdmark invitation already. Murat A. Erdogdu, , undefined... Sign in to view more. Verified email at stanford.edu - Homepage. Authors: Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Şimşekli, Murat A. Erdogdu.  Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT,  2019, Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance, Riemannian Langevin Algorithm for Solving Semidefinite Programs, An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias, On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness, Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint, Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond, Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 159–167. See the complete profile on LinkedIn and discover Murat A.’s connections and jobs at similar companies. ∙ Download PDF Abstract: Recent studies have provided both empirical and theoretical evidence illustrating that heavy tails can emerge in stochastic gradient descent (SGD) in various scenarios. Stanford University, Recent studies have provided both empirical and theoretical evidence Dicker, L. H. and Erdogdu, M. A. Watch Queue Queue. Assistant Professor of Computer Science at University of Toronto, 2018-Current. Murat A Erdogdu. Asymptotic Normality and Bias, On the Convergence of Langevin Monte Carlo: The Interplay between Tail ∙ 127, A Spectral Enabled GAN for Time Series Data Generation, 03/02/2021 ∙ by Kaleb E Smith ∙ Search Search. topo* Subject search: Truncate MSC codes with wildcard, e.g. Murat is currently a postdoctoral researcher at Microsoft Research – New England. share, We study sampling from a target distribution ν_* = e^-f using the Assistant Professor of Statistics at University of Toronto, 2018-Current. JMLR Workshop & Conference Proceedings. Abstract

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs)when the number of observations is much larger than the number of coefficients (n > > p > > 1). ... Murat A. Erdogdu, Faculty at University of Toronto Computer Science and Statistics, Co-advised with Andrea Montanari. Sched.com Conference Mobile Apps ∙ Title. degrees in Electrical Engineering and Mathematics, share, We consider the problem of minimizing a sum of n functions over a convex... 05/27/2020 ∙ by Murat A. Erdogdu, et al. CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations. share, Are you a researcher?Expose your workto one of the largestA.I. erdogdu has one repository available. Advisor Name: Montanari/Bayati Murat A Erdogdu. 0 (2016b). for Solving Large SDPs, Inference in Graphical Models via Semidefinite Programming Hierarchies, Scalable Approximations for Generalized Linear Problems, Newton-Stein Method: An optimization method for GLMs via Stein's Lemma, Convergence rates of sub-sampled Newton methods. Sched.com Conference Mobile Apps Murat A. Erdogdu retweeted. View the profiles of people named Murad Erdogdu. and B.S. 14A15 or 14A* Author search: Sequence does not matter; use of first name or initial varies by journal, e.g. Maximum likelihood for variance estimation in high-dimensional linear models. unadju... 0 Murat A. Erdogdu. ∙ Announcements •Homework 1 is due on Feb 8, 13:59. ∙ 0 Murat A. has 4 jobs listed on their profile. I wrote a blog post on a gem hidden in an 80 page paper that nobody has time to read or interpret, which imo, is … Murat A. Erdogdu. Murat A. Erdogdu. Murat A. Erdogdu is an assistant professor at the University of Toronto in Departments of Computer Science and Statistical Sciences. Murat A. Erdogdu & David Duvenaud Department of Computer Science Department of Statistical Sciences STA414/2104 Statistical Methods for Machine Learning II erdogdu at cs.toronto dot edu. 96, SUM: A Benchmark Dataset of Semantic Urban Meshes, 02/27/2021 ∙ by Weixiao Gao ∙ Toronto, ON M5S 3G4 erdogdu at cs.toronto dot edu. 0 Toronto, ON M5S 3G4 Murat-Erdogdu. share, We propose a Langevin diffusion-based algorithm for non-convex optimizat... Murat A. Erdogdu & David Duvenaud Department of Computer Science Department of Statistical Sciences Lecture 4 STA414/2104 Statistical Methods for Machine Learning II Slide credits: Russ Salakhutdinov 1. View the profiles of people named Murat Erdoğdu. Murat A. Erdogdu & David Duvenaud Department of Computer Science Department of Statistical Sciences Lecture 3 STA414/2104 Statistical Methods for Machine Learning II Slide credits: Russ Salakhutdinov. Department of Statistical Sciences Murat ERDOGDU adlı kişinin profilinde 5 iş ilanı bulunuyor. share, In stochastic optimization, the population risk is generally approximate... ∙ Search tips. Curriculum vitae. •TA Ohs will be announced. In this regime, op- Erdogdu, followers CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations. ∙ ∙ Joel Goh, Faculty at National University of Singapore (and Harvard Business School), Co-advised with Stefanos Zenios. ∙ CSC 311: Introduction to Machine Learning Lecture 4 - Linear Classification & Optimization Richard Zemel & Murat A. Erdogdu University of ∙ 11/21/2016 ∙ by Murat A. Erdogdu, et al. –You can turn in HW in class, OHs, etc. Announcements •Homework 1 is due on Jan 29 10 pm. Murat A Erdogdu. Department of Statistical Sciences Vector Institute. Vector Institute, Pratt 286b, 6 King’s College Rd. 08/12/2015 ∙ by Murat A. Erdogdu, et al. (2016b). 06/14/2020 ∙ by Lu Yu, et al. 0 share, Semidefinite programming (SDP) with equality constraints arise in many ∙ –Due on Jan 29 10 pm. Murat-Erdogdu. ∙ Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond,  2019, A. Anastasiou, K. Balasubramanian and M.A. Maximum likelihood for variance estimation in high-dimensional linear models. Murat Erdogdu Controlling & Finance and Reporting - Transaction Support Manager at Siemens München, Bayern, Deutschland Finanzdienstleistungen Each function in the starter code needs a Join Facebook to connect with Murat Erdoğdu and others you may know. share, We study sampling from a target distribution ν_* ∝ e^-f using the Riemannian Langevin Algorithm for Solving Semidefinite Programs,  2020, L. Yu, K. Balasubramanian, S. Volgushev and M.A. On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness,  2020, J. Ba, M.A. Murat ERDOGDU adlı kullanıcının dünyanın en büyük profesyonel topluluğu olan LinkedIn‘deki profilini görüntüleyin. Joel Goh, Faculty at National University of Singapore (and Harvard Business School), Co-advised with Stefanos Zenios. Newton-Stein Method: An optimization method for GLMs via Stein’s Lemma Murat A. Erdogdu Abstract We consider the problem of e ciently computing the maximum likelihood estimator Advisor Name: Montanari/Bayati 0 Graduate School of Business, Stanford University, Lee H. Dicker. Watch Queue Queue Murat ERDOGDU adlı kullanıcının dünyanın en büyük profesyonel topluluğu olan LinkedIn‘deki profilini görüntüleyin. Join Facebook to connect with Murad Erdogdu and others you may know. 07/22/2020 ∙ by Murat A. Erdogdu, et al. Department of Statistical Sciences Vector Institute. Search for Murat A Erdogdu's work. Erdogdu, Murat A. Erdogdu. I am an assistant professor at the University of Toronto in departments of Computer Science and Statistical Sciences. An icon used to represent a menu that can be toggled by interacting with this icon. Murat A Erdogdu; Affiliations. Approach, 02/16/2021 ∙ by Maha Mohammed Khan ∙ I did my Ph.D. at 147, Training Larger Networks for Deep Reinforcement Learning, 02/16/2021 ∙ by Kei Ota ∙ 0 Before, he was a postdoctoral researcher at Microsoft Research - New England. Newton-Stein Method: An optimization method for GLMs via Stein’s Lemma Murat A. Erdogdu Abstract We consider the problem of e ciently computing the maximum likelihood estimator Follow their code on GitHub. ∙ 02/20/2021 ∙ by Hongjian Wang, et al. Home Murat A Erdogdu Colleagues. I have an M.S. 07/12/2018 ∙ by Murat A. Erdogdu, et al. Dicker, L. H. and Erdogdu, M. A. In this regime, op- Under a logarithmic Sobolev inequality, we establish a guar-antee for nite iteration convergence to the Gibbs distribution in terms of Kullback{Leibler divergence. Murat A. Erdogdu is an assistant professor at the University of Toronto in Departments of Computer Science and Statistical Sciences. View Notes - lec04.pdf from CS C311 at University of Toronto. Murat A Erdogdu. communities in the world, Get the week's mostpopular data scienceresearch in your inbox -every Saturday, An Implementation of Vector Quantization using the Genetic Algorithm Check out what Murat A. Erdogdu will be attending at NIPS 2013 See what Murat A. Erdogdu will be attending and learn more about the event taking place Dec 4 - 10, 2013 in Lake Tahoe, Nevada. ... We consider the problem of minimizing a sum of n functions over a convex... Convergence Rates of Stochastic Gradient Descent under Infinite Noise Murat A. has 4 jobs listed on their profile. Murat A. Erdogdu Department of Statistics Stanford University erdogdu@stanford.edu Abstract We consider the problem of efficiently computing the maximum likelihood esti-mator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients (np1). Murat A. Erdogdu's 15 research works with 307 citations and 401 reads, including: Convergence Analysis of Langevin Monte Carlo in Chi-Square Divergence ∙ Murat A. Erdogdu's 18 research works with 334 citations and 499 reads, including: Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance Khashayar Khosravi, Postdoctoral Researcher at Google Research 06/19/2019 ∙ by Xuechen Li, et al. communities, Join one of the world's largest A.I. Mufan (Bill) Li∗ Murat A. Erdogdu† October 26, 2020 Abstract We propose a Langevin di usion-based algorithm for non-convex optimization and sampling on a product manifold of spheres. ... In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 159–167. Khashayar Khosravi, Postdoctoral Researcher at Google Research ∙ Murat A. Erdogdu, Faculty at University of Toronto Computer Science and Statistics, Co-advised with Andrea Montanari. Mufan (Bill) Li @mufan_li. ... Announcements •Homework 1 v0 is released on Jan 19! He is a faculty member of the Machine Learning Group and the Vector Institute, and a CIFAR Chair in Artificial Intelligence. Department of Statistics, Stanford University, Mohsen Bayati. Curriculum vitae. Murat A. Erdogdu retweeted. countermeasures, and way forward, 02/25/2021 ∙ by Momina Masood ∙ Department of Statistics at Stanford University Read Murat A. Erdogdu's latest research, browse their coauthor's research, and play around with their algorithms 2. Machine Learning: Theory for learning and sampling algorithms, Optimization: Non-convex, convex algorithms for machine learning, Statistics: High-dimensional data analysis, regularization and shrinkage, H. Wang, M. Gurbuzbalaban, L. Zhu, U. Simsekli and M.A. Search for Murat A Erdogdu's work. Home Murat A Erdogdu Colleagues. 0 Stanford University. This video is unavailable. Murat A. Erdogdu. Sort. He is a faculty member of the Machine Learning Group and the Vector Institute, and a CIFAR Chair in Artificial Intelligence. "integral equations" Wildcard search: Use asterisk, e.g. Stanford University (9) Microsoft Research (2) Stanford Graduate School of Business (2) Howard Hughes Medical Institute (1) ∙ If not, let me know. ∙ Download PDF Abstract: Recent studies have provided both empirical and theoretical evidence illustrating that heavy tails can emerge in stochastic gradient descent (SGD) in various scenarios. ∙ … Murat A. Erdogdu, , undefined... Sign in to view more. ∙ Murat regularly publishes at the top-rated machine learning conference NIPS, and has journal papers in the Annals of Statistics and JMLR. Academic Employment. Murat A. Erdogdu Department of Statistics Stanford University erdogdu@stanford.edu Abstract We consider the problem of efficiently computing the maximum likelihood esti-mator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients (np1). ∙ UNIVERSITY OF TORONTO share, The randomized midpoint method, proposed by [SL19], has emerged as an op... Faculty Member of the Vector Institute, 2018-Current. 87, Deepfakes Generation and Detection: State-of-the-art, open challenges, Murat A. Erdogdu's 18 research works with 334 citations and 499 reads, including: Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance Murat ERDOGDU adlı kullanıcının LinkedIn‘deki tam profili görün ve bağlantılarını ve benzer şirketlerdeki iş ilanlarını keşfedin. 127, GenoML: Automated Machine Learning for Genomics, 03/04/2021 ∙ by Mary B. Makarious ∙ Show Academic Trajectory ∙ (2016a). Dicker, L. H. and Erdogdu, M. A. Join Facebook to connect with Murad Erdogdu and others you may know. Murat A. Erdogdu's 15 research works with 307 citations and 401 reads, including: Convergence Analysis of Langevin Monte Carlo in Chi-Square Divergence Murat A Erdogdu; Affiliations. Murat A. Erdogdu Department of Computer Science Department of Statistical Sciences Lecture 4 STA414/2104 Statistical Methods for Machine Learning II 1. where I was jointly advised by Mohsen Bayati and Andrea Montanari. Erdogdu and R. Hosseinzadeh, unadju... Announcements •Homework 1 is due on Jan 29 10 pm. View the profiles of people named Murat Erdoğdu. Sampling Method, Riemannian Langevin Algorithm for Solving Semidefinite Programs, A Brief Note on the Convergence of Langevin Monte Carlo in Chi-Square •Hwis not long! 0 0 Before, he was a postdoctoral researcher at Microsoft Research - New England. Mufan (Bill) Li∗ Murat A. Erdogdu† October 26, 2020 Abstract We propose a Langevin di usion-based algorithm for non-convex optimization and sampling on a product manifold of spheres. 11/06/2020 ∙ by Ye He, et al. Under a logarithmic Sobolev inequality, we establish a guar-antee for nite iteration convergence to the Gibbs distribution in terms of Kullback{Leibler divergence. share, Despite its success in a wide range of applications, characterizing the Faculty Member of the Vector Institute, 2018-Current. ∙ Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance,  2021, M. Li and M.A. JMLR Workshop & Conference Proceedings. share, Structured non-convex learning problems, for which critical points have –You can turn in … Dicker, L. H. and Erdogdu, M. A. Mufan (Bill) Li @mufan_li. Search Search. degree in Computer Science from Stanford, Jan 13. Murat A. Erdogdu Department of Computer Science Department of Statistical Sciences Lecture 2 STA414/2104 Statistical Methods for Machine Learning II 1. Murat A. Erdogdu Department of Computer Science Department of Statistical Sciences Lecture 5 STA414/2104 Statistical Methods for Machine Learning II 1. Research Activity and News. 10/21/2020 ∙ by Mufan Bill Li, et al. Announcements •Midterm is “in class’’ 2 hrlong written exam, to be held on March 1st for Mon section, and March 2ndfor Tue section. ∙ share, An Euler discretization of the Langevin diffusion is known to converge t... Contact. ∙ Stein's Lemma and Subsampling in Large-Scale Optimization. Sort by citations Sort by year Sort by title. Murat ERDOGDU adlı kişinin profilinde 5 iş ilanı bulunuyor. Exact phrase search: Use quotes, e.g. erdogdu has one repository available. View the profiles of people named Murad Erdogdu. Applied Filters. Pratt 286b, 6 King’s College Rd. Murat A. Erdogdu. Erdogdu, M.A. o... Variance, On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Join Facebook to connect with Murat Erdoğdu and others you may know. harris john or t arens Diacritics: Drop diacritics, e.g. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Erdogdu, Cited by. Divergence, Hausdorff Dimension, Stochastic Differential Equations, and I wrote a blog post on a gem hidden in an 80 page paper that nobody has time to read or interpret, which imo, is … share, Sampling with Markov chain Monte Carlo methods typically amounts to

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