| 2023 | ALT | Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. | Anant Raj, Melih Barsbey, Mert Grbzbalaban, Lingjiong Zhu, Umut Simsekli |
| 2023 | ICML | Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. | Anant Raj, Lingjiong Zhu, Mert Grbzbalaban, Umut Simsekli |
| 2022 | SC | HyLo: A Hybrid Low-Rank Natural Gradient Descent Method. | Baorun Mu, Saeed Soori, Bugra Can, Mert Grbzbalaban, Maryam Mehri Dehnavi |
| 2021 | AISTATS | Fractional moment-preserving initialization schemes for training deep neural networks. | Mert Grbzbalaban, Yuanhan Hu |
| 2021 | ICML | Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections. | Alexander Camuto, Xiaoyu Wang, Lingjiong Zhu, Chris C. Holmes, Mert Grbzbalaban, Umut Simsekli |
| 2021 | ICML | The Heavy-Tail Phenomenon in SGD. | Mert Grbzbalaban, Umut Simsekli, Lingjiong Zhu |
| 2020 | AISTATS | DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. | Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Grbzbalaban |
| 2020 | ICML | Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. | Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Grbzbalaban |
| 2019 | ICML | Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. | Bugra Can, Mert Grbzbalaban, Lingjiong Zhu |
| 2019 | ICML | A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. | Umut Simsekli, Levent Sagun, Mert Grbzbalaban |
| 2018 | ICPP | Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems. | Saeed Soori, Aditya Devarakonda, Zachary Blanco, James Demmel, Mert Grbzbalaban, Maryam Mehri Dehnavi |
| 2017 | ICASSP | A double incremental aggregated gradient method with linear convergence rate for large-scale optimization. | Aryan Mokhtari, Mert Grbzbalaban, Alejandro Ribeiro |