| Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games J Han, R Hu Mathematical and scientific machine learning, 221-245, 2020 | 55 | 2020 |
| Optimal portfolio under fast mean-reverting fractional stochastic environment JP Fouque, R Hu SIAM Journal on Financial Mathematics 9 (2), 564-601, 2018 | 48 | 2018 |
| Deep fictitious play for stochastic differential games R Hu arXiv preprint arXiv:1903.09376, 2019 | 42 | 2019 |
| Recent developments in machine learning methods for stochastic control and games R Hu, M Lauriere arXiv preprint arXiv:2303.10257, 2023 | 40 | 2023 |
| Signatured deep fictitious play for mean field games with common noise M Min, R Hu arXiv preprint arXiv:2106.03272, 2021 | 38 | 2021 |
| Optimal portfolio under fractional stochastic environment JP Fouque, R Hu Mathematical Finance 29 (3), 697-734, 2019 | 37 | 2019 |
| Recurrent neural networks for stochastic control problems with delay J Han, R Hu Mathematics of Control, Signals, and Systems 33 (4), 775-795, 2021 | 34 | 2021 |
| Sequential design for ranking response surfaces R Hu, M Ludkovski SIAM/ASA Journal on Uncertainty Quantification 5 (1), 212-239, 2017 | 33 | 2017 |
| N-player and mean-field games in Itô-diffusion markets with competitive or homophilous interaction R Hu, T Zariphopoulou Stochastic Analysis, Filtering, and Stochastic Optimization: A Commemorative …, 2022 | 30 | 2022 |
| Learning high-dimensional McKean–Vlasov forward-backward stochastic differential equations with general distribution dependence J Han, R Hu, J Long SIAM Journal on Numerical Analysis 62 (1), 1-24, 2024 | 28 | 2024 |
| Convergence of deep fictitious play for stochastic differential games J Han, R Hu, J Long arXiv preprint arXiv:2008.05519, 2020 | 26 | 2020 |
| Asymptotic optimal strategy for portfolio optimization in a slowly varying stochastic environment JP Fouque, R Hu SIAM Journal on Control and Optimization 55 (3), 1990-2023, 2017 | 21 | 2017 |
| Deep learning for ranking response surfaces with applications to optimal stopping problems R Hu Quantitative Finance 20 (9), 1567-1581, 2020 | 14 | 2020 |
| Higher-order error estimates for physics-informed neural networks approximating the primitive equations R Hu, Q Lin, A Raydan, S Tang Partial Differential Equations and Applications 4 (4), 34, 2023 | 13 | 2023 |
| Optimal policies for a pandemic: A stochastic game approach and a deep learning algorithm Y Xuan, R Balkin, J Han, R Hu, HD Ceniceros Mathematical and Scientific Machine Learning, 987-1012, 2022 | 13 | 2022 |
| Convergence of the backward deep BSDE method with applications to optimal stopping problems C Gao, S Gao, R Hu, Z Zhu SIAM Journal on Financial Mathematics 14 (4), 1290-1303, 2023 | 12 | 2023 |
| A deep learning analysis of climate change, innovation, and uncertainty M Barnett, W Brock, LP Hansen, R Hu, J Huang arXiv preprint arXiv:2310.13200, 2023 | 12 | 2023 |
| Systemic risk and optimal fee for central clearing counterparty under partial netting Z Cui, Q Feng, R Hu, B Zou Operations Research Letters 46 (3), 306-311, 2018 | 12* | 2018 |
| A class of dimensionality-free metrics for the convergence of empirical measures J Han, R Hu, J Long arXiv preprint arXiv:2104.12036 196, 2021 | 10 | 2021 |
| Multiscale asymptotic analysis for portfolio optimization under stochastic environment JP Fouque, R Hu Multiscale Modeling & Simulation 18 (3), 1318-1342, 2020 | 10 | 2020 |
Jiequn HanFlatiron Institute, Simons FoundationVerified email at flatironinstitute.org