| Decentralized stochastic gradient Langevin dynamics and Hamiltonian monte carlo M Gürbüzbalaban, X Gao, Y Hu, L Zhu Journal of Machine Learning Research 22 (239), 1-69, 2021 | 25 | 2021 |
| Fractional moment-preserving initialization schemes for training deep neural networks M Gurbuzbalaban, Y Hu International Conference on Artificial Intelligence and Statistics, 2233-2241, 2021 | 19* | 2021 |
| Non-convex optimization via non-reversible stochastic gradient Langevin dynamics Y Hu, X Wang, X Gao, M Gurbuzbalaban, L Zhu arXiv preprint arXiv:2004.02823, 2020 | 16 | 2020 |
| Heavy-tail phenomenon in decentralized sgd M Gürbüzbalaban, Y Hu, U Şimşekli, K Yuan, L Zhu IISE Transactions, 1-15, 2024 | 5 | 2024 |
| Penalized Langevin and Hamiltonian Monte Carlo Algorithms for Constrained Sampling M Gürbüzbalaban, Y Hu, L Zhu arXiv preprint arXiv:2212.00570, 2022 | 5 | 2022 |
| Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling M Gurbuzbalaban, Y Hu, L Zhu Journal of Machine Learning Research 25 (263), 1-67, 2024 | 2 | 2024 |
| Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD. M Gürbüzbalaban, Y Hu, U Simsekli, L Zhu CoRR, 2023 | 2* | 2023 |
| Stochastic Gradient and Stochastic Gradient MCMC Methods for Bayesian Learning and Non-convex Optimization: Centralized and Decentralized Settings Y Hu Rutgers The State University of New Jersey, Graduate School-Newark, 2023 | | 2023 |
Mert GürbüzbalabanRutgers UniversityVerified email at business.rutgers.edu