Murat A. Erdogdu

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 63

Top Topics

Convergence Rate

Top Venues

Publications

Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. IEEE Trans. Inf. Theory 70 (1) (2024)
Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan Bill Li
Sampling from the Mean-Field Stationary Distribution. CoRR (2024)
Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li
Sampling from the Mean-Field Stationary Distribution. COLT (2024)
Nuri Mert Vural, Murat A. Erdogdu
Pruning is Optimal for Learning Sparse Features in High-Dimensions. CoRR (2024)
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
Minimax Linear Regression under the Quantile Risk. COLT (2024)
Nuri Mert Vural, Murat A. Erdogdu
Pruning is Optimal for Learning Sparse Features in High-Dimensions. COLT (2024)
Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
Gradient-Based Feature Learning under Structured Data. NeurIPS (2023)
Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu
Improved Discretization Analysis for Underdamped Langevin Monte Carlo. COLT (2023)
Alireza Mousavi Hosseini, Tyler K. Farghly, Ye He, Krishna Balasubramanian, Murat A. Erdogdu
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality. COLT (2023)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective. NeurIPS (2023)
Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning. NeurIPS (2023)
Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning. CoRR (2023)
Avital Shafran, Ilia Shumailov, Murat A. Erdogdu, Nicolas Papernot
Beyond Labeling Oracles: What does it mean to steal ML models? CoRR (2023)
Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. CoRR (2023)
Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. ICLR (2023)
Alireza Mousavi Hosseini, Denny Wu, Taiji Suzuki, Murat A. Erdogdu
Gradient-Based Feature Learning under Structured Data. CoRR (2023)
Ayoub El Hanchi, Murat A. Erdogdu
Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression. NeurIPS (2023)
Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations. CoRR (2022)
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. COLT (2022)
Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. CoRR (2022)
Matthew Shunshi Zhang, Murat A. Erdogdu, Animesh Garg
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. AAAI (2022)
Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. COLT (2022)
Sejun Park, Umut Simsekli, Murat A. Erdogdu
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers. NeurIPS (2022)
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. CoRR (2022)
Murat A. Erdogdu, Asuman E. Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli
Convergence rate of block-coordinate maximization Burer-Monteiro method for solving large SDPs. Math. Program. 195 (1) (2022)
Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
Understanding the Variance Collapse of SVGD in High Dimensions. ICLR (2022)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. NeurIPS (2022)
Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang
Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. COLT (2022)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. CoRR (2022)
Sejun Park, Umut Simsekli, Murat A. Erdogdu
Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers. CoRR (2022)
Murat A. Erdogdu, Rasa Hosseinzadeh, Shunshi Zhang
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. AISTATS (2022)
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson
Manipulating SGD with Data Ordering Attacks. NeurIPS (2021)
Murat A. Erdogdu, Rasa Hosseinzadeh
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness. COLT (2021)
Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. CoRR (2021)
Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. NeurIPS (2021)
Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. NeurIPS (2021)
Abhishek Roy, Krishnakumar Balasubramanian, Murat A. Erdogdu
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. NeurIPS (2021)
Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gürbüzbalaban, Umut Simsekli, Lingjiong Zhu
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. CoRR (2021)
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson
Manipulating SGD with Data Ordering Attacks. CoRR (2021)
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. NeurIPS (2021)
Hongjian Wang, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Simsekli, Murat A. Erdogdu
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. NeurIPS (2021)
Mufan Bill Li, Murat A. Erdogdu
Riemannian Langevin Algorithm for Solving Semidefinite Programs. CoRR (2020)
Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang
Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. ICLR (2020)
Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu
Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks. CoRR (2020)
Murat A. Erdogdu, Rasa Hosseinzadeh
A Brief Note on the Convergence of Langevin Monte Carlo in Chi-Square Divergence. CoRR (2020)
Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. NeurIPS (2020)
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. CoRR (2020)
Murat A. Erdogdu, Rasa Hosseinzadeh
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness. CoRR (2020)
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. NeurIPS (2020)
Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. CoRR (2020)
Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker
Scalable Approximations for Generalized Linear Problems. J. Mach. Learn. Res. 20 (2019)
Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu
Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. COLT (2019)
Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. CoRR (2019)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. CoRR (2018)
Murat A. Erdogdu, Asuman E. Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli
Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDPs. CoRR (2018)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. NeurIPS (2018)
Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Inference in Graphical Models via Semidefinite Programming Hierarchies. NIPS (2017)
Hakan Inan, Murat A. Erdogdu, Mark J. Schnitzer
Robust Estimation of Neural Signals in Calcium Imaging. NIPS (2017)
Murat A. Erdogdu
Generalized Hessian approximations via Stein's lemma for constrained minimization. ITA (2017)
Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Inference in Graphical Models via Semidefinite Programming Hierarchies. CoRR (2017)
Murat A. Erdogdu
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma. J. Mach. Learn. Res. 17 (2016)
Lee H. Dicker, Murat A. Erdogdu
Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models. AISTATS (2016)
Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati
Scaled Least Squares Estimator for GLMs in Large-Scale Problems. NIPS (2016)
Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, Jure Leskovec
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. CoRR (2015)
Murat A. Erdogdu, Andrea Montanari
Convergence rates of sub-sampled Newton methods. NIPS (2015)
Murat A. Erdogdu, Nadia Fawaz, Andrea Montanari
Privacy-Utility Trade-Off for Time-Series with Application to Smart-Meter Data. AAAI Workshop: Computational Sustainability (2015)
Murat A. Erdogdu
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma. NIPS (2015)
Murat A. Erdogdu, Nadia Fawaz
Privacy-utility trade-off under continual observation. ISIT (2015)
Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, Jure Leskovec
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. KDD (2015)
Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
Estimating LASSO Risk and Noise Level. NIPS (2013)