Molei Tao

Orcid: 0000-0002-3308-6176

According to our database1, Molei Tao authored at least 36 papers between 2010 and 2024.

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Bibliography

2024
Convergence of Kinetic Langevin Monte Carlo on Lie groups.
CoRR, 2024

Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion.
CoRR, 2024

DFU: scale-robust diffusion model for zero-shot super-resolution image generation.
CoRR, 2024

Automated construction of effective potential via algorithmic implicit bias.
CoRR, 2024

2023
NySALT: Nyström-type inference-based schemes adaptive to large time-stepping.
J. Comput. Phys., March, 2023

Markov Chain Monte Carlo for Gaussian: A Linear Control Perspective.
IEEE Control. Syst. Lett., 2023

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult.
CoRR, 2023

Extragradient Type Methods for Riemannian Variational Inequality Problems.
CoRR, 2023

Mirror Diffusion Models for Constrained and Watermarked Generation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Momentum Multi-Marginal Schrödinger Bridge.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

gDDIM: Generalized denoising diffusion implicit models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Parametric Resonance for Enhancing the Rate of Metastable Transition.
SIAM J. Appl. Math., June, 2022

Accurate and efficient simulations of Hamiltonian mechanical systems with discontinuous potentials.
J. Comput. Phys., 2022

Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian.
CoRR, 2022

Alternating Mirror Descent for Constrained Min-Max Games.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Hessian-Free High-Resolution Nesterov Acceleration For Sampling.
Proceedings of the International Conference on Machine Learning, 2022

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Sqrt(d) Dimension Dependence of Langevin Monte Carlo.
Proceedings of the Tenth International Conference on Learning Representations, 2022

The Mirror Langevin Algorithm Converges with Vanishing Bias.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021
A Derivative-Free Optimization Method With Application to Functions With Exploding and Vanishing Gradients.
IEEE Control. Syst. Lett., 2021

Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps.
Proceedings of the 38th International Conference on Machine Learning, 2021

Variational Symplectic Accelerated Optimization on Lie Groups.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020
AhKin: A modular and efficient code for the Doppler shift attenuation method.
Comput. Phys. Commun., 2020

Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients.
CoRR, 2020

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Data-driven multiscale decompositions for forecasting and model discovery.
CoRR, 2019

Simply improved averaging for coupled oscillators and weakly nonlinear waves.
Commun. Nonlinear Sci. Numer. Simul., 2019

Parametric Resonant Control of Macroscopic Behaviors of Multiple Oscillators.
Proceedings of the 2019 American Control Conference, 2019

2016
Explicit high-order symplectic integrators for charged particles in general electromagnetic fields.
J. Comput. Phys., 2016

2015
Convex Optimal Uncertainty Quantification.
SIAM J. Optim., 2015

2013
Variational integrators for electric circuits.
J. Comput. Phys., 2013

Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids.
Proceedings of the American Control Conference, 2013

2010
Nonintrusive and Structure Preserving Multiscale Integration of Stiff ODEs, SDEs, and Hamiltonian Systems with Hidden Slow Dynamics via Flow Averaging.
Multiscale Model. Simul., 2010


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