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 | 46 | 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 | 22 | 2021 |
Joint trajectory inference for single-cell genomics using deep learning with a mixture prior JH Du, T Chen, M Gao, J Wang Proceedings of the National Academy of Sciences 121 (37), e2316256121, 2024 | 18* | 2024 |
Efficient Bayesian network structure learning via local Markov boundary search M Gao, B Aragam Advances in Neural Information Processing Systems 34, 4301-4313, 2021 | 16 | 2021 |
Optimal estimation of Gaussian DAG models M Gao, WM Tai, B Aragam International Conference on Artificial Intelligence and Statistics, 8738-8757, 2022 | 14 | 2022 |
Multivariate change point detection for heterogeneous series Y Guo, M Gao, X Lu Neurocomputing 510, 122-134, 2022 | 4 | 2022 |
Optimal estimation of Gaussian (poly) trees Y Wang, M Gao, WM Tai, B Aragam, A Bhattacharyya International Conference on Artificial Intelligence and Statistics, 3619-3627, 2024 | 1 | 2024 |
Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach M Gao, C Zhang Available at SSRN 4545015, 2023 | 1 | 2023 |
Optimal neighbourhood selection in structural equation models M Gao, WM Tai, B Aragam arXiv preprint arXiv:2306.02244, 2023 | | 2023 |