Molei Tao
Orcid: 0000-0002-3308-6176
According to our database1,
Molei Tao
authored at least 61 papers
between 2010 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2025
CoRR, August, 2025
Theory-Informed Improvements to Classifier-Free Guidance for Discrete Diffusion Models.
CoRR, July, 2025
CoRR, June, 2025
CoRR, June, 2025
Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond.
CoRR, February, 2025
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms.
CoRR, February, 2025
SIAM J. Sci. Comput., 2025
Appropriate State-Dependent Friction Coefficient Accelerates Kinetic Langevin Dynamics.
SIAM J. Appl. Math., 2025
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025
Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025
2024
J. Comput. Phys., 2024
CoRR, 2024
Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks.
CoRR, 2024
DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations.
CoRR, 2024
CoRR, 2024
CoRR, 2024
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
J. Comput. Phys., March, 2023
IEEE Control. Syst. Lett., 2023
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
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
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
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
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
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
2020
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
CoRR, 2019
Commun. Nonlinear Sci. Numer. Simul., 2019
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
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