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Mean-Square Analysis with An Application to Optimal Dimension Dependence of Langevin Monte Carlo


Sep 08, 2021
Ruilin Li, Hongyuan Zha, Molei Tao

* Submitted to NeurIPS 2021 on May 28, 2021 (the submission deadline) 

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Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps


Mar 09, 2021
Renyi Chen, Molei Tao

* Comments are welcome 

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Hessian-Free High-Resolution Nesterov Acceleration for Sampling


Jun 22, 2020
Ruilin Li, Hongyuan Zha, Molei Tao


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Hessian-Free High-Resolution Nesterov Accelerationfor Sampling


Jun 16, 2020
Ruilin Li, Hongyuan Zha, Molei Tao


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Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients


Feb 20, 2020
Ruilin Li, Xin Wang, Hongyuan Zha, Molei Tao


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Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective


Feb 14, 2020
Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao


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Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function


Feb 14, 2020
Lingkai Kong, Molei Tao

* Comments are welcome 

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Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems


Jan 27, 2020
Molei Tao, Tomoki Ohsawa

* Accepted by AISTATS 2020; never submitted elsewhere 

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