| Seismic: A self-exciting point process model Q Zhao, MA Erdogdu, HY He, A Rajaraman, J Leskovec Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 795 | 2015 |
| Convergence rates of sub-sampled Newton methods MA Erdogdu, A Montanari Advances in Neural Information Processing Systems, 1090-1098, 2015 | 186 | 2015 |
| High-dimensional asymptotics of feature learning: How one gradient step improves the representation J Ba, MA Erdogdu, T Suzuki, Z Wang, D Wu, G Yang Advances in Neural Information Processing Systems 35, 37932-37946, 2022 | 153 | 2022 |
| Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev S Chewi, MA Erdogdu, MB Li, R Shen, M Zhang Foundations of Computational Mathematics, 2024 | 129 | 2024 |
| Global non-convex optimization with discretized diffusions MA Erdogdu, L Mackey, O Shamir Advances in Neural Information Processing Systems 31, 2018 | 124 | 2018 |
| Manipulating sgd with data ordering attacks I Shumailov, Z Shumaylov, D Kazhdan, Y Zhao, N Papernot, MA Erdogdu, ... Advances in Neural Information Processing Systems 34, 18021-18032, 2021 | 94 | 2021 |
| Generalization of two-layer neural networks: An asymptotic viewpoint J Ba, M Erdogdu, T Suzuki, D Wu, T Zhang International conference on learning representations, 2020 | 92 | 2020 |
| Convergence rates of active learning for maximum likelihood estimation K Chaudhuri, SM Kakade, P Netrapalli, S Sanghavi Advances in Neural Information Processing Systems 28, 2015 | 86 | 2015 |
| Stochastic runge-kutta accelerates langevin monte carlo and beyond X Li, Y Wu, L Mackey, MA Erdogdu Advances in neural information processing systems 32, 2019 | 82 | 2019 |
| Towards a theory of non-log-concave sampling: first-order stationarity guarantees for langevin monte carlo K Balasubramanian, S Chewi, MA Erdogdu, A Salim, S Zhang Conference on Learning Theory, 2896-2923, 2022 | 78 | 2022 |
| On the convergence of langevin monte carlo: The interplay between tail growth and smoothness MA Erdogdu, R Hosseinzadeh Conference on Learning Theory, 1776-1822, 2021 | 78 | 2021 |
| Estimating lasso risk and noise level M Bayati, MA Erdogdu, A Montanari Advances in Neural Information Processing Systems 26, 2013 | 73 | 2013 |
| Hausdorff dimension, heavy tails, and generalization in neural networks U Simsekli, O Sener, G Deligiannidis, MA Erdogdu Advances in Neural Information Processing Systems, 2020 | 69* | 2020 |
| Neural networks efficiently learn low-dimensional representations with sgd A Mousavi-Hosseini, S Park, M Girotti, I Mitliagkas, MA Erdogdu International Conference on Learning Representations, 2022 | 61 | 2022 |
| An analysis of constant step size sgd in the non-convex regime: Asymptotic normality and bias L Yu, K Balasubramanian, S Volgushev, MA Erdogdu Advances in Neural Information Processing Systems, 2021 | 51 | 2021 |
| Convergence rates of stochastic gradient descent under infinite noise variance H Wang, M Gurbuzbalaban, L Zhu, U Simsekli, MA Erdogdu Advances in Neural Information Processing Systems 34, 18866-18877, 2021 | 48 | 2021 |
| Normal approximation for stochastic gradient descent via non-asymptotic rates of martingale CLT A Anastasiou, K Balasubramanian, MA Erdogdu Conference on Learning Theory, 115-137, 2019 | 47 | 2019 |
| Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence MA Erdogdu, R Hosseinzadeh, MS Zhang International Conference on Artificial Intelligence and Statistics, 2021 | 46 | 2021 |
| Robust estimation of neural signals in calcium imaging H Inan, MA Erdogdu, M Schnitzer Advances in neural information processing systems 30, 2017 | 42 | 2017 |
| On the ergodicity, bias and asymptotic normality of randomized midpoint sampling method Y He, K Balasubramanian, MA Erdogdu Advances in Neural Information Processing Systems 33, 7366-7376, 2020 | 41 | 2020 |