Combining MetaMap and cTAKES in Disorder Recognition: THCIB at CLEF eHealth Lab 2013 Task 1. - Yunqing Xia; Xiaoshi Zhong; Peng Liu; Cheng Tan 007; Sen Na; Qinan Hu; Yaohai Huang (CLEF (Working Notes), 2013)

Normalization of Abbreviations/Acronyms: THCIB at CLEF eHealth 2013 Task 2. - Yunqing Xia; Xiaoshi Zhong; Peng Liu; Cheng Tan 007; Sen Na; Qinan Hu; Yaohai Huang (CLEF (Working Notes), 2013)

Concept-based Medical Document Retrieval: THCIB at CLEF eHealth Lab 2013 Task 3. - Xiaoshi Zhong; Yunqing Xia; Zhongda Xie; Sen Na; Qinan Hu; Yaohai Huang (CLEF (Working Notes), 2013)

Ranking Search Intents Underlying a Query. - Yunqing Xia; Xiaoshi Zhong; Guoyu Tang; Junjun Wang; Qiang Zhou; Thomas Fang Zheng; Qinan Hu; Sen Na; Yaohai Huang (NLDB, 2013)

Understanding the Query: THCIB and THUIS at NTCIR-10 Intent Task. - Junjun Wang; Guoyu Tang; Yunqing Xia; Qiang Zhou; Thomas Fang Zheng; Qinan Hu; Sen Na; Yaohai Huang (NTCIR, 2013)

Stochastic Strictly Contractive Peaceman-Rachford Splitting Method. - Sen Na; Mingyuan Ma; Shuming Ma; Guangju Peng (CoRR, 2017)

High-dimensional Index Volatility Models via Stein's Identity. - Sen Na; Mladen Kolar (CoRR, 2018)

The Graph-based Broad Behavior-Aware Recommendation System for Interactive News. - Mingyuan Ma; Sen Na; Cong Xu; Xin Fan (CoRR, 2018)

A New Learning-Based One Shot Detection Framework for Natural Images. - Sen Na; Ruoyu Yan (ICANN (3), 2019)

High-dimensional Varying Index Coefficient Models via Stein's Identity. - Sen Na; Zhuoran Yang; Zhaoran Wang 001; Mladen Kolar (J. Mach. Learn. Res., 2019)

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. - Sen Na; Yuwei Luo; Zhuoran Yang; Zhaoran Wang 001; Mladen Kolar (ICML, 2020)

Overlapping Schwarz Decomposition for Nonlinear Optimal Control. - Sen Na; Sungho Shin; Mihai Anitescu; Victor M. Zavala (CoRR, 2020)

AEGCN: An Autoencoder-Constrained Graph Convolutional Network. - Mingyuan Ma; Sen Na; Hongyu Wang 006; Jin Xu 002 (CoRR, 2020)

Exponential Decay in the Sensitivity Analysis of Nonlinear Dynamic Programming. - Sen Na; Mihai Anitescu (SIAM J. Optim., 2020)

Global Convergence of Online Optimization for Nonlinear Model Predictive Control. - Sen Na (NeurIPS, 2021)

AEGCN: An Autoencoder-Constrained Graph Convolutional Network. - Mingyuan Ma; Sen Na; Hongyu Wang 006 (Neurocomputing, 2021)

The graph-based behavior-aware recommendation for interactive news. - Mingyuan Ma; Sen Na; Hongyu Wang 006; Congzhou Chen; Jin Xu 002 (Appl. Intell., 2022)

SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction. - Mingyuan Ma; Sen Na; Xiaolu Zhang; Congzhou Chen; Jin Xu (Briefings Bioinform., 2022)

Asymptotic Convergence Rate and Statistical Inference for Stochastic Sequential Quadratic Programming. - Sen Na; Michael W. Mahoney (CoRR, 2022)

Near-Optimal Performance of Stochastic Predictive Control. - Sungho Shin; Sen Na; Mihai Anitescu (CoRR, 2022)

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control. - Sen Na; Sungho Shin; Mihai Anitescu; Victor M. Zavala (IEEE Trans. Autom. Control., 2022)

Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. - Ilgee Hong; Sen Na; Michael W. Mahoney; Mladen Kolar (ICML, 2023)

Hessian averaging in stochastic Newton methods achieves superlinear convergence. - Sen Na; Michal Derezinski; Michael W. Mahoney (Math. Program., 2023)

Superconvergence of Online Optimization for Model Predictive Control. - Sen Na; Mihai Anitescu (IEEE Trans. Autom. Control., 2023)

An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Program., 2023)

Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Program., 2023)

Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming. - Xiaoran Cheng; Sen Na (CoRR, 2024)

Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (CoRR, 2024)

Convergence Analysis of Accelerated Stochastic Gradient Descent Under the Growth Condition. - You-Lin Chen; Sen Na; Mladen Kolar (Math. Oper. Res., 2024)

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Oper. Res., 2024)

Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (SIAM J. Optim., 2024)

Online Covariance Estimation in Nonsmooth Stochastic Approximation. - Liwei Jiang; Abhishek Roy; Krishna Balasubramanian; Damek Davis; Dmitriy Drusvyatskiy; Sen Na (COLT, 2025)

Online Covariance Matrix Estimation in Sketched Newton Methods. - Wei Kuang; Mihai Anitescu; Sen Na (CoRR, 2025)

High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise. - Yuchen Fang; Javad Lavaei; Katya Scheinberg; Sen Na (CoRR, 2025)

Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling. - Xinchen Du; Wanrong Zhu; Wei Biao Wu; Sen Na (CoRR, 2025)

Panda: partially approximate newton methods for distributed minimax optimization with unbalanced dimensions. - Minheng Xiao; Chengchang Liu; Cheng Chen 015; John C. S. Lui; Sen Na (Mach. Learn., 2025)