4 citations · 10 across the 6 of their papers we have counts for
12 papers
The Mirror Langevin Algorithm Converges with Vanishing Bias
Ruilin Li, Molei Tao, Santosh S. Vempala +1
The technique of modifying the geometry of a problem from Euclidean to Hessian metric has proved to be quite effective in optimization, and has been the subject of study for sampli…
Parametric resonance for enhancing the rate of metastable transition
Ying Chao, Molei Tao
This work is devoted to quantifying how periodic perturbation can change the rate of metastable transition in stochastic mechanical systems with weak noises. A closed-form explicit…
Variational Symplectic Accelerated Optimization on Lie Groups
Taeyoung Lee, Molei Tao, Melvin Leok
There has been significant interest in generalizations of the Nesterov accelerated gradient descent algorithm due to its improved performance guarantee compared to the standard gra…
GRIT: a package for structure-preserving simulations of gravitationally interacting rigid-bodies
Renyi Chen, Gongjie Li, Molei Tao
Spin-orbit coupling of planetary systems plays an important role in the dynamics and habitability of planets. However, symplectic integrators that can accurately simulate not only…
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
Renyi Chen, Molei Tao
We consider the learning and prediction of nonlinear time series generated by a latent symplectic map. A special case is (not necessarily separable) Hamiltonian systems, whose solu…
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
Kaixuan Huang, Yuqing Wang, Molei Tao +1
Deep residual networks (ResNets) have demonstrated better generalization performance than deep feedforward networks (FFNets). However, the theory behind such a phenomenon is still…