Murat A. Erdogdu

According to our database, Murat A. Erdogdu authored at least 51 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling.

Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu

IEEE Trans. Inf. Theory, January, 2024

Sampling from the Mean-Field Stationary Distribution.

Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan Bill Li

CoRR, 2024

2023

Beyond Labeling Oracles: What does it mean to steal ML models?

Avital Shafran, Ilia Shumailov, Murat A. Erdogdu, Nicolas Papernot

CoRR, 2023

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling.

Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu

CoRR, 2023

Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning.

Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Gradient-Based Feature Learning under Structured Data.

Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression.

Ayoub El Hanchi, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Networks Efficiently Learn Low-Dimensional Representations with SGD.

Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improved Discretization Analysis for Underdamped Langevin Monte Carlo.

Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality.

Alireza Mousavi Hosseini, Tyler K. Farghly, Ye He, Krishna Balasubramanian, Murat A. Erdogdu

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Convergence rate of block-coordinate maximization Burer-Monteiro method for solving large SDPs.

Murat A. Erdogdu, Asuman E. Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli

Math. Program., 2022

Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers.

Sejun Park, Umut Simsekli, Murat A. Erdogdu

CoRR, 2022

p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations.

Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot

CoRR, 2022

Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers.

Sejun Park, Umut Simsekli, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding the Variance Collapse of SVGD in High Dimensions.

Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance.

Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev.

Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo.

Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence.

Murat A. Erdogdu, Rasa Hosseinzadeh, Shunshi Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings.

Matthew Shunshi Zhang, Murat A. Erdogdu, Animesh Garg

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias.

Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance.

Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Manipulating SGD with Data Ordering Attacks.

Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Empirical Risk Minimization with Dependent and Heavy-Tailed Data.

Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms.

Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks.

Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness.

Murat A. Erdogdu, Rasa Hosseinzadeh

Proceedings of the Conference on Learning Theory, 2021

2020

Riemannian Langevin Algorithm for Solving Semidefinite Programs.

Mufan (Bill) Li, Murat A. Erdogdu

CoRR, 2020

A Brief Note on the Convergence of Langevin Monte Carlo in Chi-Square Divergence.

Murat A. Erdogdu, Rasa Hosseinzadeh

CoRR, 2020

Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks.

Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu

CoRR, 2020

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks.

Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method.

Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint.

Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Scalable Approximations for Generalized Linear Problems.

Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker

J. Mach. Learn. Res., 2019

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond.

Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu

CoRR, 2019

Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT.

Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu

Proceedings of the Conference on Learning Theory, 2019

2018

Global Non-convex Optimization with Discretized Diffusions.

Murat A. Erdogdu, Lester Mackey, Ohad Shamir

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Robust Estimation of Neural Signals in Calcium Imaging.

Hakan Inan, Murat A. Erdogdu, Mark J. Schnitzer

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Inference in Graphical Models via Semidefinite Programming Hierarchies.

Murat A. Erdogdu, Yash Deshpande, Andrea Montanari

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Generalized Hessian approximations via Stein's lemma for constrained minimization.

Murat A. Erdogdu

Proceedings of the 2017 Information Theory and Applications Workshop, 2017

2016

Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma.

Murat A. Erdogdu

J. Mach. Learn. Res., 2016

Scaled Least Squares Estimator for GLMs in Large-Scale Problems.

Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models.

Lee H. Dicker, Murat A. Erdogdu

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Convergence rates of sub-sampled Newton methods.

Murat A. Erdogdu, Andrea Montanari

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma.

Murat A. Erdogdu

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity.

Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, Jure Leskovec

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Privacy-utility trade-off under continual observation.

Murat A. Erdogdu, Nadia Fawaz

Proceedings of the IEEE International Symposium on Information Theory, 2015

Privacy-Utility Trade-Off for Time-Series with Application to Smart-Meter Data.

Murat A. Erdogdu, Nadia Fawaz, Andrea Montanari

Proceedings of the Computational Sustainability, 2015

2013

Estimating LASSO Risk and Noise Level.

Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013