| A polynomial-time algorithm for learning nonparametric causal graphs M Gao, Y Ding, B Aragam Advances in Neural Information Processing Systems 33, 11599-11611, 2020 | 34 | 2020 |
| Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families G Rajendran, B Kivva, M Gao, B Aragam Advances in Neural Information Processing Systems 34, 18660-18672, 2021 | 18 | 2021 |
| Efficient Bayesian network structure learning via local Markov boundary search M Gao, B Aragam Advances in Neural Information Processing Systems 34, 4301-4313, 2021 | 14 | 2021 |
| Model-based trajectory inference for single-cell rna sequencing using deep learning with a mixture prior JH Du, T Chen, M Gao, J Wang bioRxiv, 2020.12. 26.424452, 2020 | 13 | 2020 |
| Optimal estimation of Gaussian DAG models M Gao, WM Tai, B Aragam International Conference on Artificial Intelligence and Statistics, 8738-8757, 2022 | 10 | 2022 |
| Multivariate change point detection for heterogeneous series Y Guo, M Gao, X Lu Neurocomputing 510, 122-134, 2022 | 2 | 2022 |
| Optimal estimation of Gaussian (poly) trees Y Wang, M Gao, WM Tai, B Aragam, A Bhattacharyya arXiv preprint arXiv:2402.06380, 2024 | | 2024 |
| Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach M Gao, C Zhang Available at SSRN 4545015, 2023 | | 2023 |
| Optimal neighbourhood selection in structural equation models M Gao, WM Tai, B Aragam arXiv preprint arXiv:2306.02244, 2023 | | 2023 |
