Sen Na
PhD (2016-2021)
Sen Na was a PhD student in the Department of Statistics at The University of Chicago. Prior to graduate school, he obtained BS in mathematics at Nanjing University, China. His research interests lie in nonlinear and nonconvex optimization, dynamic programming, high-dimensional statistics and their interface.
Interests
- Nonlinear Optimization
- Dynamic Programming
- High-Dimensional Statistics
Education
- PhD in Statistics, 2021University of Chicago
- BS in Mathematics, 2016Nanjing University
Publications
Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
Technical report
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
Technical report
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
SIAM Journal on Optimization, 34(2), 2007-2037
Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
Mathematics of Operations Research, 49(4), 2492–2526
A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
Mathematics of Operations Research, 49(2), 1012–1044
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
Mathematical Programming, 202(1), 279–353
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
International Conference on Machine Learning (ICML)
An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians
Mathematical Programming
Estimating differential latent variable graphical models with applications to brain connectivity
Biometrika, 108(2)
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Proceedings of the 37th International Conference on Machine Learning (ICML) 2020
High-dimensional Varying Index Coefficient Models via Stein's Identity
Journal of Machine Learning Research