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2020 – today
- 2023
[j9]Xingjie Li, Fei Lu, Molei Tao, Felix X.-F. Ye:
NySALT: Nyström-type inference-based schemes adaptive to large time-stepping. J. Comput. Phys. 477: 111952 (2023)- [i17]Tianrong Chen, Guan-Horng Liu, Molei Tao, Evangelos A. Theodorou:
Deep Momentum Multi-Marginal Schrödinger Bridge. CoRR abs/2303.01751 (2023) - 2022
- [j8]Molei Tao
, Shi Jin:
Accurate and efficient simulations of Hamiltonian mechanical systems with discontinuous potentials. J. Comput. Phys. 450: 110846 (2022) - [c12]Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono:
The Mirror Langevin Algorithm Converges with Vanishing Bias. ALT 2022: 718-742 - [c11]Ruilin Li, Hongyuan Zha, Molei Tao:
Sqrt(d) Dimension Dependence of Langevin Monte Carlo. ICLR 2022 - [c10]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. ICLR 2022 - [c9]Ruilin Li, Hongyuan Zha, Molei Tao:
Hessian-Free High-Resolution Nesterov Acceleration For Sampling. ICML 2022: 13125-13162 - [c8]Andre Wibisono, Molei Tao, Georgios Piliouras:
Alternating Mirror Descent for Constrained Min-Max Games. NeurIPS 2022 - [i16]Lingkai Kong, Yuqing Wang, Molei Tao:
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. CoRR abs/2205.14173 (2022) - [i15]Andre Wibisono, Molei Tao, Georgios Piliouras:
Alternating Mirror Descent for Constrained Min-Max Games. CoRR abs/2206.04160 (2022) - [i14]Qinsheng Zhang, Molei Tao, Yongxin Chen:
gDDIM: Generalized denoising diffusion implicit models. CoRR abs/2206.05564 (2022) - [i13]Xingjie Li, Fei Lu, Molei Tao, Felix X.-F. Ye:
NySALT: Nyström-type inference-based schemes adaptive to large time-stepping. CoRR abs/2207.06012 (2022) - [i12]Oswin So, Gongjie Li, Evangelos A. Theodorou, Molei Tao:
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian. CoRR abs/2210.00090 (2022) - 2021
- [j7]Said Al-Abri, Tony X. Lin, Molei Tao, Fumin Zhang:
A Derivative-Free Optimization Method With Application to Functions With Exploding and Vanishing Gradients. IEEE Control. Syst. Lett. 5(2): 587-592 (2021) - [c7]Taeyoung Lee, Molei Tao, Melvin Leok:
Variational Symplectic Accelerated Optimization on Lie Groups. CDC 2021: 233-240 - [c6]Renyi Chen, Molei Tao:
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps. ICML 2021: 1717-1727 - [i11]Molei Tao, Shi Jin:
Accurate and Efficient Simulations of Hamiltonian Mechanical Systems with Discontinuous Potentials. CoRR abs/2101.11018 (2021) - [i10]Renyi Chen, Molei Tao:
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps. CoRR abs/2103.05632 (2021) - [i9]Ruilin Li, Hongyuan Zha, Molei Tao:
Sqrt(d) Dimension Dependence of Langevin Monte Carlo. CoRR abs/2109.03839 (2021) - [i8]Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono:
The Mirror Langevin Algorithm Converges with Vanishing Bias. CoRR abs/2109.12077 (2021) - [i7]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. CoRR abs/2110.03677 (2021) - 2020
- [j6]Alejandro Garzón, Wilmar Rodriguez, Fernando Cristancho, Molei Tao:
AhKin: A modular and efficient code for the Doppler shift attenuation method. Comput. Phys. Commun. 246 (2020) - [c5]Molei Tao, Tomoki Ohsawa:
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems. AISTATS 2020: 4269-4280 - [c4]Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. NeurIPS 2020 - [c3]Lingkai Kong, Molei Tao:
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. NeurIPS 2020 - [i6]Molei Tao, Tomoki Ohsawa:
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems. CoRR abs/2001.10006 (2020) - [i5]Lingkai Kong, Molei Tao:
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function. CoRR abs/2002.06189 (2020) - [i4]Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. CoRR abs/2002.06262 (2020) - [i3]Ruilin Li, Xin Wang, Hongyuan Zha, Molei Tao:
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients. CoRR abs/2002.08949 (2020) - [i2]Ruilin Li, Hongyuan Zha, Molei Tao:
Hessian-Free High-Resolution Nesterov Acceleration for Sampling. CoRR abs/2006.09230 (2020)
2010 – 2019
- 2019
- [j5]Molei Tao:
Simply improved averaging for coupled oscillators and weakly nonlinear waves. Commun. Nonlinear Sci. Numer. Simul. 71: 1-21 (2019) - [c2]Pengcheng Xie, Molei Tao:
Parametric Resonant Control of Macroscopic Behaviors of Multiple Oscillators. ACC 2019: 1898-1905 - [i1]Daniel Dylewsky, Molei Tao, J. Nathan Kutz:
Data-driven multiscale decompositions for forecasting and model discovery. CoRR abs/1903.12480 (2019) - 2016
- [j4]Molei Tao:
Explicit high-order symplectic integrators for charged particles in general electromagnetic fields. J. Comput. Phys. 327: 245-251 (2016) - 2015
- [j3]Shuo Han, Molei Tao, Ufuk Topcu, Houman Owhadi, Richard M. Murray:
Convex Optimal Uncertainty Quantification. SIAM J. Optim. 25(3): 1368-1387 (2015) - 2013
- [j2]Sina Ober-Blöbaum, Molei Tao, Mulin Cheng, Houman Owhadi, Jerrold E. Marsden:
Variational integrators for electric circuits. J. Comput. Phys. 242: 498-530 (2013) - [c1]Shuo Han, Ufuk Topcu, Molei Tao, Houman Owhadi, Richard M. Murray:
Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids. ACC 2013: 1130-1137 - 2010
- [j1]Molei Tao, Houman Owhadi, Jerrold E. Marsden:
Nonintrusive and Structure Preserving Multiscale Integration of Stiff ODEs, SDEs, and Hamiltonian Systems with Hidden Slow Dynamics via Flow Averaging. Multiscale Model. Simul. 8(4): 1269-1324 (2010)
Coauthor Index
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last updated on 2023-05-13 03:55 CEST by the dblp team
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