Estimating LASSO Risk and Noise Level. - Mohsen Bayati; Murat A. Erdogdu; Andrea Montanari (NIPS, 2013)

Privacy-Utility Trade-Off for Time-Series with Application to Smart-Meter Data. - Murat A. Erdogdu; Nadia Fawaz; Andrea Montanari (AAAI Workshop: Computational Sustainability, 2015)

Privacy-utility trade-off under continual observation. - Murat A. Erdogdu; Nadia Fawaz (ISIT, 2015)

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. - Qingyuan Zhao; Murat A. Erdogdu; Hera Y. He; Anand Rajaraman; Jure Leskovec (KDD, 2015)

Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma. - Murat A. Erdogdu (NIPS, 2015)

Convergence rates of sub-sampled Newton methods. - Murat A. Erdogdu; Andrea Montanari (NIPS, 2015)

Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models. - Lee H. Dicker; Murat A. Erdogdu (AISTATS, 2016)

Scaled Least Squares Estimator for GLMs in Large-Scale Problems. - Murat A. Erdogdu; Lee H. Dicker; Mohsen Bayati (NIPS, 2016)

Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma. - Murat A. Erdogdu (J. Mach. Learn. Res., 2016)

Generalized Hessian approximations via Stein's lemma for constrained minimization. - Murat A. Erdogdu (ITA, 2017)

Inference in Graphical Models via Semidefinite Programming Hierarchies. - Murat A. Erdogdu; Yash Deshpande; Andrea Montanari (NIPS, 2017)

Robust Estimation of Neural Signals in Calcium Imaging. - Hakan Inan; Murat A. Erdogdu; Mark J. Schnitzer (NIPS, 2017)

Global Non-convex Optimization with Discretized Diffusions. - Murat A. Erdogdu; Lester Mackey; Ohad Shamir (NeurIPS, 2018)

Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. - Andreas Anastasiou; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (COLT, 2019)

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. - Xuechen Li; Denny Wu; Lester Mackey; Murat A. Erdogdu (CoRR, 2019)

Scalable Approximations for Generalized Linear Problems. - Murat A. Erdogdu; Mohsen Bayati; Lee H. Dicker (J. Mach. Learn. Res., 2019)

Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Denny Wu; Tianzong Zhang (ICLR, 2020)

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (NeurIPS, 2020)

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks. - Umut Simsekli; Ozan Sener; George Deligiannidis; Murat A. Erdogdu (NeurIPS, 2020)

Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks. - Umut Simsekli; Ozan Sener; George Deligiannidis; 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)

Riemannian Langevin Algorithm for Solving Semidefinite Programs. - Mufan (Bill) Li; Murat A. Erdogdu (CoRR, 2020)

On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness. - Murat A. Erdogdu; Rasa Hosseinzadeh (COLT, 2021)

Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks. - Melih Barsbey; Milad Sefidgaran; Murat A. Erdogdu; Gaël Richard; Umut Simsekli (NeurIPS, 2021)

Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms. - Alexander Camuto; George Deligiannidis; Murat A. Erdogdu; Mert Gürbüzbalaban; Umut Simsekli; Lingjiong Zhu (NeurIPS, 2021)

On Empirical Risk Minimization with Dependent and Heavy-Tailed Data. - Abhishek Roy 005; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (NeurIPS, 2021)

Manipulating SGD with Data Ordering Attacks. - Ilia Shumailov; Zakhar Shumaylov; Dmitry Kazhdan; Yiren Zhao; Nicolas Papernot; Murat A. Erdogdu; Ross J. Anderson (NeurIPS, 2021)

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance. - Hongjian Wang; Mert Gürbüzbalaban; Lingjiong Zhu; Umut Simsekli; Murat A. Erdogdu (NeurIPS, 2021)

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias. - Lu Yu; Krishnakumar Balasubramanian 002; Stanislav Volgushev; Murat A. Erdogdu (NeurIPS, 2021)

Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. - Matthew Shunshi Zhang; Murat A. Erdogdu; Animesh Garg (AAAI, 2022)

Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. - Murat A. Erdogdu; Rasa Hosseinzadeh; Shunshi Zhang 001 (AISTATS, 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 001 (COLT, 2022)

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. - Nuri Mert Vural; Lu Yu; Krishnakumar Balasubramanian 002; Stanislav Volgushev; Murat A. Erdogdu (COLT, 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 (ICLR, 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 (NeurIPS, 2022)

Generalization Bounds for Stochastic Gradient Descent via Localized $ varepsilon$-Covers. - Sejun Park; Umut Simsekli; Murat A. Erdogdu (NeurIPS, 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 ε-Covers. - Sejun Park; Umut Simsekli; Murat A. Erdogdu (CoRR, 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)

Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality. - Alireza Mousavi Hosseini; Tyler K. Farghly; Ye He 003; Krishna Balasubramanian; Murat A. Erdogdu (COLT, 2023)

Improved Discretization Analysis for Underdamped Langevin Monte Carlo. - Matthew Shunshi Zhang; Sinho Chewi; Mufan (Bill) Li; Krishna Balasubramanian; Murat A. Erdogdu (COLT, 2023)

Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. - Alireza Mousavi Hosseini; Sejun Park; Manuela Girotti; Ioannis Mitliagkas; Murat A. Erdogdu (ICLR, 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 (NeurIPS, 2023)

Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression. - Ayoub El Hanchi; Murat A. Erdogdu (NeurIPS, 2023)

Gradient-Based Feature Learning under Structured Data. - Alireza Mousavi Hosseini; Denny Wu; Taiji Suzuki; Murat A. Erdogdu (NeurIPS, 2023)

Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning. - Tyler Kastner; Murat A. Erdogdu; Amir-massoud Farahmand (NeurIPS, 2023)

Minimax Linear Regression under the Quantile Risk. - Ayoub El Hanchi; Chris J. Maddison; Murat A. Erdogdu (COLT, 2024)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li (COLT, 2024)

Pruning is Optimal for Learning Sparse Features in High-Dimensions. - Nuri Mert Vural; Murat A. Erdogdu (COLT, 2024)

A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers. - Ye He 003; Alireza Mousavi Hosseini; Krishnakumar Balasubramanian 001; Murat A. Erdogdu (NeurIPS, 2024)

On the Efficiency of ERM in Feature Learning. - Ayoub El Hanchi; Chris J. Maddison; Murat A. Erdogdu (NeurIPS, 2024)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan Bill Li (CoRR, 2024)

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling. - Ye He 003; Tyler Farghly; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (J. Mach. Learn. Res., 2024)

Beyond Labeling Oracles - What does it mean to steal ML models? - Avital Shafran; Ilia Shumailov; Murat A. Erdogdu; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling. - Ye He 003; Krishnakumar Balasubramanian 002; Murat A. Erdogdu (IEEE Trans. Inf. Theory, 2024)

Robust Feature Learning for Multi-Index Models in High Dimensions. - Alireza Mousavi Hosseini; Adel Javanmard; Murat A. Erdogdu (ICLR, 2025)

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. - Alireza Mousavi Hosseini; Denny Wu; Murat A. Erdogdu (ICLR, 2025)

When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective. - Alireza Mousavi Hosseini; Clayton Sanford; Denny Wu; Murat A. Erdogdu (CoRR, 2025)

Distributional Training Data Attribution. - Bruno Mlodozeniec; Isaac Reid; Sam Power; David Krueger 001; Murat A. Erdogdu; Richard E. Turner; Roger B. Grosse (CoRR, 2025)

Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev. - Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li; Ruoqi Shen; Matthew Shunshi Zhang (Found. Comput. Math., 2025)