1. Online Statistical Inference for Proximal Stochastic Gradient Descent under Markovian Sampling
Xinchen Du, Sen Na
A short version has been accepted in NeurIPS 2025 COML Workshop, 2025.

2. Statistical Inference of Constrained Model Estimation via Derivative-Free Stochastic Sequential Quadratic Programming
Jianliang Ye, Sen Na
A short version has been accepted in NeurIPS 2025 COML Workshop, 2025.

1. Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
Sayantan Choudhury, Xiaoran Cheng, Martin Takáč, Sen Na, Mladen Kolar
arXiv preprint arXiv:2605.06884, 2026.

2. A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
Yuchen Fang, Jihun Kim, Sen Na, James Demmel, Javad Lavaei
arXiv preprint arXiv:2603.10230, 2026.

3. Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
Yihang Gao, Michael K. Ng, Michael W. Mahoney, Sen Na
arXiv preprint arXiv:2512.08948, 2025.

4. Overlapping Schwarz Scheme for Linear-Quadratic Programs in Continuous Time
Hongli Zhao, Mihai Anitescu, Sen Na
arXiv preprint arXiv:2510.04478, 2025.

5. Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Xinchen Du, Wanrong Zhu, Wei Biao Wu, Sen Na
arXiv preprint arXiv:2505.18327, 2025.

1. Inference of Online Newton Methods with Nesterov's Accelerated Sketching
Haoxuan Wang, Xinchen Du, Sen Na
International Conference on Machine Learning, 2026.

2. Derivative-Free Sequential Quadratic Programming for Equality-Constrained Stochastic Optimization
Sen Na
Proceedings of INFORMS Optimization Society Conference (under journal review), 2026.

3. High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise
Yuchen Fang, Javad Lavaei, Sen Na
Mathematical Programming, 2026.

4. Online Covariance Matrix Estimation in Sketched Newton Methods
Wei Kuang, Mihai Anitescu, Sen Na
Information and Inference: a journal of the IMA, 2026.

5. Near-Optimal Performance of Stochastic Model Predictive Control
Sungho Shin, Sen Na, Mihai Anitescu
Mathematics of Operations Research, 2026.

6. Online Covariance Estimation in Nonsmooth Stochastic Approximation
Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na
Conference on Learning Theory, 2025.

7. Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Sen Na, Michael W. Mahoney
Journal of Machine Learning Research, 2025.

8. Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar
SIAM Journal on Optimization, 2024.

9. Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
You-Lin Chen, Sen Na, Mladen Kolar
Mathematics of Operations Research, 2023.

10. A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
Sen Na, Mihai Anitescu, Mladen Kolar
Mathematics of Operations Research, 2023.

11. Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
Sen Na, Mihai Anitescu, Mladen Kolar
Mathematical Programming, 2023.

12. Superconvergence of Online Optimization for Model Predictive Control
Sen Na, Mihai Anitescu
IEEE Transactions on Automatic Control, 2023.

13. Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
Sen Na, Michał Dereziński, Michael W. Mahoney
Mathematical Programming, 2022.

14. On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
Sen Na, Sungho Shin, Mihai Anitescu, Victor M. Zavala
IEEE Transactions on Automatic Control, 2022.

15. An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
Sen Na, Mihai Anitescu, Mladen Kolar
Mathematical Programming, 2022.