A consensus-based global optimization method for high dimensional machine learning problems JA Carrillo, S Jin, L Li, Y Zhu
ESAIM: Control, Optimisation and Calculus of Variations 27, S5, 2021
146 2021 Hypocoercivity and Uniform Regularity for the Vlasov--Poisson--Fokker--Planck System with Uncertainty and Multiple Scales S Jin, Y Zhu
SIAM journal on Mathematical Analysis 50 (2), 1790-1816, 2018
55 2018 The Vlasov--Poisson--Fokker--Planck system with uncertainty and a one-dimensional asymptotic preserving method Y Zhu, S Jin
Multiscale Modeling & Simulation 15 (4), 1502-1529, 2017
53 2017 Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients J An, L Ying, Y Zhu
arXiv preprint arXiv:2009.13447, 2020
46 2020 FedCBO: Reaching group consensus in clustered federated learning through consensus-based optimization JA Carrillo, NG Trillos, S Li, Y Zhu
Journal of machine learning research 25 (214), 1-51, 2024
21 2024 Towards theoretical understanding of large batch training in stochastic gradient descent X Dai, Y Zhu
arXiv preprint arXiv:1812.00542, 2018
11 2018 Borrowing from the future: An attempt to address double sampling Y Zhu, L Ying
Mathematical and scientific machine learning, 246-268, 2020
9 2020 An interacting particle consensus method for constrained global optimization JA Carrillo, S Jin, H Zhang, Y Zhu
arXiv preprint arXiv:2405.00891, 2024
8 2024 Borrowing from the future: Addressing double sampling in model-free control Y Zhu, Z Izzo, L Ying
Mathematical and Scientific Machine Learning, 1099-1136, 2022
6 2022 The Vlasov–Fokker–Planck equation with high dimensional parametric forcing term S Jin, Y Zhu, E Zuazua
Numerische Mathematik 150 (2), 479-519, 2022
6 2022 A note on optimization formulations of Markov decision processes L Ying, Y Zhu
arXiv preprint arXiv:2012.09417, 2020
6 2020 Optimal factor analysis and applications to content-based image retrieval Y Zhu, W Mio, X Liu
International Conference on Computer Vision and Computer Graphics, 164-176, 2007
6 2007 On Large Batch Training and Sharp Minima: A Fokker–Planck Perspective X Dai, Y Zhu
Journal of Statistical Theory and Practice 14 (3), 1-31, 2020
5 2020 On Bellman equations for continuous-time policy evaluation I: discretization and approximation W Mou, Y Zhu
arXiv preprint arXiv:2407.05966, 2024
4 2024 A local sensitivity and regularity analysis for the Vlasov-Poisson-Fokker-Planck system with multi-dimensional uncertainty and the spectral convergence of the stochastic … Y Zhu
Networks and Heterogeneous Media 14 (4), 677-707, 2019
3 2019 CB NG Trillos, S Li, K Riedl, Y Zhu
arXiv preprint arXiv:2411.13394, 2024
2 2024 PhiBe: A PDE-based Bellman equation for continuous time policy evaluation Y Zhu
arXiv preprint arXiv:2405.12535, 2024
2 2024 Continuous-in-time limit for Bayesian bandits Y Zhu, Z Izzo, L Ying
Journal of Machine Learning Research 24 (305), 1-35, 2023
2 2023 A sharp convergence rate for a model equation of the asynchronous stochastic gradient descent Y Zhu, L Ying
Communications in Mathematical Sciences 19 (3), 851-863, 2021
2 2021 Boundary Control of Vlasov--Fokker--Planck Equations M Herty, S Jin, Y Zhu
arXiv preprint arXiv:2002.04215, 2020
2 2020 
Lexing YingProfessor of Mathematics, Stanford UniversityVerified email at stanford.edu
