Papers

Asterisk (*) after last author denotes alphabetical ordering.

Preprints

• Y. Kook, M. Zhang, S. Chewi, M.A. Erdogdu, M. Li, Sampling from the Mean-Field Stationary Distribution, arXiv 2024

Publications

• Y. He, T. Farghly, K. Balasubramanian, and M.A. Erdogdu, Mean-square Analysis of Discretized Ito Diffusions for Heavy-tailed Sampling, JMLR 2024, Journal of Machine Learning Research

• A. Mousavi, D. Wu, T. Suzuki, M.A. Erdogdu, Gradient-Based Feature Learning under Structured Data, NeurIPS 2023, Proceedings of Advances in Neural Information Processing Systems

• A. El Hanchi, M.A. Erdogdu, Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression, NeurIPS 2023, Proceedings of Advances in Neural Information Processing Systems

• J. Ba, M.A. Erdogdu, T. Suzuki, Z. Wang, D. Wu*, Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective, NeurIPS 2023, Proceedings of Advances in Neural Information Processing Systems

• T. Kastner, M.A. Erdogdu, A. Farahmand, Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning NeurIPS 2023, Proceedings of Advances in Neural Information Processing Systems

• Y. He, K. Balasubramanian, M.A. Erdogdu, An analysis of Transformed Unadjusted Langevin Algorithm for Heavy-tailed Sampling, IEEE Transactions on Information Theory, 2023

• A. Mousavi-Hosseini, T. Farghly, Y. He, K. Balasubramanian, M.A. Erdogdu, Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincare Inequality, COLT 2023, Proceedings of Annual Conference on Learning Theory

• M.S. Zhang, S. Chewi, M. Li, K. Balasubramanian, M.A. Erdogdu, Improved Discretization Analysis for Underdamped Langevin Monte Carlo, COLT 2023, Proceedings of Annual Conference on Learning Theory

• A. Mousavi-Hosseini, S. Park, M. Girotti, I. Mitliagkas, M.A. Erdogdu, Neural Networks Efficiently Learn Low-Dimensional Representations with SGD, ICLR 2023 (Spotlight), Proceedings of International Conference on Learning Representations

• M. Li and M.A. Erdogdu, Riemannian Langevin Algorithm for Solving Semidefinite Programs, Bernoulli Journal 2023

• S. Park, U. Simsekli, M.A. Erdogdu, Generalization Bounds for Stochastic Gradient Descent via Localized \(\epsilon\)-Covers, NeurIPS 2022, Proceedings of Advances in Neural Information Processing Systems

• J. Ba, M.A. Erdogdu, T. Suzuki, Z. Wang, D. Wu, G. Yang*, High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation, NeurIPS 2022, Proceedings of Advances in Neural Information Processing Systems

• N.M. Vural, L. Yu, K. Balasubramanian, S. Volgushev and M.A. Erdogdu, Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance, COLT 2022, Proceedings of Annual Conference on Learning Theory

• S. Chewi, M.A. Erdogdu, M. Li, R. Shen, and M.S. Zhang*, Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev, COLT 2022, Annual Conference on Learning Theory (long abstract)

• K. Balasubramanian, S. Chewi, M.A. Erdogdu, A. Salim and M.S. Zhang*, Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo, COLT 2022, Proceedings of Annual Conference on Learning Theory

• J. Ba, M.A. Erdogdu, M. Ghassemi, T. Suzuki, S. Sun, D. Wu and T. Zhang*, Understanding the Variance Collapse of SVGD in High Dimensions, ICLR 2022, Proceedings of International Conference on Learning Representations

• M.A. Erdogdu, R. Hosseinzadeh and M.S. Zhang*, Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence, AISTATS 2022, Proceedings of International Conference on Artificial Intelligence and Statistics

• M.S. Zhang, M.A. Erdogdu, and A. Garg, Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings, AAAI 2022, Proceedings of the Association for the Advancement of Artificial Intelligence

• M.A. Erdogdu, A. Ozdaglar, P. Parillo, and N.D. Vanli*, Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDPs, Mathematical Programming 2021

  • Conference version ICML Workshop 2019, Modern trends in nonconvex optimization

• A. Roy, K. Balasubramanian, and M.A. Erdogdu, On Empirical Risk Minimization with Dependent and Heavy-Tailed Data, NeurIPS 2021, Proceedings of Advances in Neural Information Processing Systems

• A. Camuto, G. Deligiannidis, M.A. Erdogdu, M. Gurbuzbalaban, U. Simsekli, L. Zhu*, Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms, NeurIPS 2021 (Spotlight), Proceedings of Advances in Neural Information Processing Systems

• H. Wang, M. Gurbuzbalaban, L. Zhu, U. Simsekli and M.A. Erdogdu, Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance, NeurIPS 2021, Proceedings of Advances in Neural Information Processing Systems

• 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, NeurIPS 2021, Proceedings of Advances in Neural Information Processing Systems

• I. Shumailov, Z. Shumaylov, D. Kazhdan, Y. Zhao, N. Papernot, M.A. Erdogdu, and R. Anderson, Manipulating SGD with Data Ordering Attacks, NeurIPS 2021, Proceedings of Advances in Neural Information Processing Systems

• M. Barsbey, M. Sefidgaran, M.A. Erdogdu, G. Richard, U. Simsekli, Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks, NeurIPS 2021, Proceedings of Advances in Neural Information Processing Systems

• M.A. Erdogdu and R. Hosseinzadeh*, On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness, COLT 2021, Proceedings of Annual Conference on Learning Theory

• U. Simsekli, O. Sener, G. Deligiannidis, M.A. Erdogdu, Hausdorff Dimension, Stochastic Differential Equations, and Generalization in Neural Networks, NeurIPS 2020 (Spotlight), Proceedings of Advances in Neural Information Processing Systems
  

  • Journal version JSTAT 2022 (Invited), Journal of Statistical Mechanics: Theory and Experiment

• Y. He, K. Balasubramanian and M.A. Erdogdu, On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method, NeurIPS 2020, Proceedings of Advances in Neural Information Processing Systems

• J. Ba, M.A. Erdogdu, T. Suzuki, D. Wu and T. Zhang*, Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint, ICLR 2020 (Spotlight), Proceedings of International Conference on Learning Representations

• M.A. Erdogdu, M. Bayati and L.H. Dicker, Scalable Approximations for Generalized Linear Problems, JMLR 2019, Journal of Machine Learning Research

• A. Anastasiou, K. Balasubramanian and M.A. Erdogdu*, Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT, COLT 2019, Proceedings of Annual Conference on Learning Theory

• J. Ba, M.A. Erdogdu, M. Ghassemi, T. Suzuki, S. Sun, D. Wu and T. Zhang*, Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms, AABI 2019, Symposium on Advances in Approximate Bayesian Inference

• X. Li, D. Wu, L. Mackey and M.A. Erdogdu, Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond, NeurIPS 2019 (Spotlight), Proceedings of Advances in Neural Information Processing Systems

• M.A. Erdogdu, L. Mackey and O. Shamir*, Global Non-convex Optimization with Discretized Diffusions, NeurIPS 2018, Proceedings of Advances in Neural Information Processing Systems

• L.H. Dicker and M.A. Erdogdu*, Flexible results for quadratic forms with applications to variance components estimation, Annals of Statistics 2018

• M.A. Erdogdu, Y. Deshpande and A. Montanari, Inference in Graphical Models via Semidefinite Programming Hierarchies, NeurIPS 2017, Proceedings of Advances in Neural Information Processing Systems

• H. Inan, M.A. Erdogdu and M. Schnitzer, Robust Estimation of Neural Signals in Calcium Imaging, NeurIPS 2017, Proceedings of Advances in Neural Information Processing Systems

• M.A. Erdogdu, Newton-Stein Method: An optimization method for GLMs via Stein's lemma, JMLR 2016, Journal of Machine Learning Research

• M.A. Erdogdu, M. Bayati and L.H. Dicker, Scaled Least Squares Estimator for GLMs in Large-Scale Problems, NeurIPS 2016, Proceedings of Advances in Neural Information Processing Systems

• L.H. Dicker and M.A. Erdogdu*, Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models, AISTATS 2016, Proceedings of International Conference on Artificial Intelligence and Statistics

• M.A. Erdogdu, Newton-Stein Method: A second order method for GLMs via Stein's lemma, NeurIPS 2015 (Spotlight), Proceedings of Advances in Neural Information Processing Systems

• M.A. Erdogdu and A. Montanari*, Convergence rates of sub-sampled Newton methods, NeurIPS 2015, Proceedings of Advances in Neural Information Processing Systems

• R. Kolte, M.A. Erdogdu and A. Ozgur, Accelerating SVRG via second-order information, NeurIPS 2015, Advances in Neural Information Processing Systems - Workshop OptML

• Q. Zhao, M.A. Erdogdu, H. He, A. Rajaraman and J. Leskovec, SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity, KDD 2015, Proceedings of Conference on Knowledge Discovery and Data Mining

• M.A. Erdogdu and N. Fawaz, Privacy-utility trade-off under continual observation, ISIT 2015, Proceedings of IEEE International Symposium on Information Theory

• M.A. Erdogdu, N. Fawaz and A. Montanari, Privacy-utility trade-off for time-series with application to smart-meter data, AAAI 2015, Association for the Advancement in Artificial Intelligence, Workshop in Computational Sustainability

• M. Bayati, M.A. Erdogdu and A. Montanari*, Estimating LASSO Risk and Noise Level, NeurIPS 2013, Proceedings of Advances in Neural Information Processing Systems

Thesis

• M.A. Erdogdu, Stein's Lemma and Subsampling in Large-Scale Optimization, Thesis 2017, Stanford University