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Molei Tao
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Journal Articles
- 2024
- [j12]Xingjie Helen Li, Molei Tao:
Automated construction of effective potential via algorithmic implicit bias. J. Comput. Phys. 514: 113206 (2024) - 2023
- [j11]Bo Yuan
, Jiaojiao Fan
, Yuqing Wang
, Molei Tao
, Yongxin Chen
:
Markov Chain Monte Carlo for Gaussian: A Linear Control Perspective. IEEE Control. Syst. Lett. 7: 2173-2178 (2023) - [j10]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) - 2022
- [j9]Molei Tao
, Shi Jin:
Accurate and efficient simulations of Hamiltonian mechanical systems with discontinuous potentials. J. Comput. Phys. 450: 110846 (2022) - [j8]Ying Chao
, Molei Tao:
Parametric Resonance for Enhancing the Rate of Metastable Transition. SIAM J. Appl. Math. 82(3): 1068-1090 (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) - 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) - 2019
- [j5]Molei Tao:
Simply improved averaging for coupled oscillators and weakly nonlinear waves. Commun. Nonlinear Sci. Numer. Simul. 71: 1-21 (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) - 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)
Conference and Workshop Papers
- 2024
- [c18]Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao:
Extragradient Type Methods for Riemannian Variational Inequality Problems. AISTATS 2024: 2080-2088 - [c17]Lingkai Kong, Molei Tao:
Convergence of Kinetic Langevin Monte Carlo on Lie groups. COLT 2024: 3011-3063 - 2023
- [c16]Lingkai Kong, Yuqing Wang, Molei Tao:
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. ICLR 2023 - [c15]Qinsheng Zhang, Molei Tao, Yongxin Chen:
gDDIM: Generalized denoising diffusion implicit models. ICLR 2023 - [c14]Tianrong Chen, Guan-Horng Liu, Molei Tao, Evangelos A. Theodorou:
Deep Momentum Multi-Marginal Schrödinger Bridge. NeurIPS 2023 - [c13]Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou, Molei Tao:
Mirror Diffusion Models for Constrained and Watermarked Generation. NeurIPS 2023 - 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 - 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 - 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 - 2019
- [c2]Pengcheng Xie, Molei Tao:
Parametric Resonant Control of Macroscopic Behaviors of Multiple Oscillators. ACC 2019: 1898-1905 - 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
Informal and Other Publications
- 2024
- [i30]Xingjie Helen Li, Molei Tao:
Automated construction of effective potential via algorithmic implicit bias. CoRR abs/2401.03511 (2024) - [i29]Alexander Havrilla, Kevin Rojas, Wenjing Liao, Molei Tao:
DFU: scale-robust diffusion model for zero-shot super-resolution image generation. CoRR abs/2401.06144 (2024) - [i28]Ye He, Kevin Rojas, Molei Tao:
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion. CoRR abs/2402.17886 (2024) - [i27]Lingkai Kong, Molei Tao:
Convergence of Kinetic Langevin Monte Carlo on Lie groups. CoRR abs/2403.12012 (2024) - [i26]Yuchen Zhu, Tianrong Chen, Evangelos A. Theodorou, Xie Chen, Molei Tao:
Quantum State Generation with Structure-Preserving Diffusion Model. CoRR abs/2404.06336 (2024) - [i25]Yuchen Zhu, Tianrong Chen, Lingkai Kong, Evangelos A. Theodorou, Molei Tao:
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups. CoRR abs/2405.16381 (2024) - [i24]Lingkai Kong, Molei Tao:
Quantitative Convergences of Lie Group Momentum Optimizers. CoRR abs/2405.20390 (2024) - [i23]Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Hao Wang, Molei Tao, Dimitris N. Metaxas:
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models. CoRR abs/2405.21050 (2024) - [i22]Yuqing Wang, Ye He, Molei Tao:
Evaluating the design space of diffusion-based generative models. CoRR abs/2406.12839 (2024) - [i21]Xingjie Li, Fei Lu, Molei Tao, Felix X.-F. Ye:
Robust First and Second-Order Differentiation for Regularized Optimal Transport. CoRR abs/2407.02015 (2024) - 2023
- [i20]Tianrong Chen, Guan-Horng Liu, Molei Tao, Evangelos A. Theodorou:
Deep Momentum Multi-Marginal Schrödinger Bridge. CoRR abs/2303.01751 (2023) - [i19]Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao:
Extragradient Type Methods for Riemannian Variational Inequality Problems. CoRR abs/2309.14155 (2023) - [i18]Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou, Molei Tao:
Mirror Diffusion Models for Constrained and Watermarked Generation. CoRR abs/2310.01236 (2023) - [i17]Yuqing Wang, Zhenghao Xu, Tuo Zhao, Molei Tao:
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult. CoRR abs/2310.17087 (2023) - 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
- [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
- [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) - 2019
- [i1]Daniel Dylewsky, Molei Tao, J. Nathan Kutz:
Data-driven multiscale decompositions for forecasting and model discovery. CoRR abs/1903.12480 (2019)
Coauthor Index
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