| 2025 | ICLR | Robust Feature Learning for Multi-Index Models in High Dimensions. | Alireza Mousavi-Hosseini, Adel Javanmard, Murat A. Erdogdu |
| 2025 | ICLR | Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics. | Alireza Mousavi-Hosseini, Denny Wu, Murat A. Erdogdu |
| 2025 | ICML | Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics. | Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A. Erdogdu, Amir-massoud Farahmand |
| 2024 | COLT | Minimax Linear Regression under the Quantile Risk. | Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu |
| 2024 | COLT | Sampling from the Mean-Field Stationary Distribution. | Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li |
| 2024 | COLT | Pruning is Optimal for Learning Sparse Features in High-Dimensions. | Nuri Mert Vural, Murat A. Erdogdu |
| 2023 | COLT | Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincar Inequality. | Alireza Mousavi-Hosseini, Tyler K. Farghly, Ye He, Krishna Balasubramanian, Murat A. Erdogdu |
| 2023 | COLT | Improved Discretization Analysis for Underdamped Langevin Monte Carlo. | Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu |
| 2023 | ICLR | Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. | Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu |
| 2022 | AAAI | Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. | Matthew Shunshi Zhang, Murat A. Erdogdu, Animesh Garg |
| 2022 | AISTATS | Convergence of Langevin Monte Carlo in Chi-Squared and Rnyi Divergence. | Murat A. Erdogdu, Rasa Hosseinzadeh, Shunshi Zhang |
| 2022 | COLT | 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 |
| 2022 | COLT | Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. | Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang |
| 2022 | COLT | Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. | Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu |
| 2022 | ICLR | Understanding the Variance Collapse of SVGD in High Dimensions. | Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang |
| 2021 | COLT | On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness. | Murat A. Erdogdu, Rasa Hosseinzadeh |
| 2020 | ICLR | Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint. | Jimmy Ba, Murat A. Erdogdu, Taiji Suzuki, Denny Wu, Tianzong Zhang |
| 2019 | COLT | Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT. | Andreas Anastasiou, Krishnakumar Balasubramanian, Murat A. Erdogdu |
| 2017 | ITA | Generalized Hessian approximations via Stein's lemma for constrained minimization. | Murat A. Erdogdu |
| 2016 | AISTATS | Maximum Likelihood for Variance Estimation in High-Dimensional Linear Models. | Lee H. Dicker, Murat A. Erdogdu |
| 2015 | AAAI | Privacy-Utility Trade-Off for Time-Series with Application to Smart-Meter Data. | Murat A. Erdogdu, Nadia Fawaz, Andrea Montanari |
| 2015 | ISIT | Privacy-utility trade-off under continual observation. | Murat A. Erdogdu, Nadia Fawaz |
| 2015 | KDD | SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. | Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, Jure Leskovec |