New RIC Bounds via l_q-minimization with 0<q<=1 in Compressed Sensing. - Shenglong Zhou 001; Lingchen Kong; Ziyan Luo; Naihua Xiu (CoRR, 2013)

Exact Recovery for Sparse Signal via Weighted l_1 Minimization. - Shenglong Zhou 001; Naihua Xiu; Yingnan Wang; Lingchen Kong (CoRR, 2013)

A half thresholding projection algorithm for sparse solutions of LCPs. - Meijuan Shang; Chao Zhang; Dingtao Peng; Shenglong Zhou 001 (Optim. Lett., 2015)

A Fast Matrix Majorization-Projection Method for Penalized Stress Minimization With Box Constraints. - Shenglong Zhou 001; Naihua Xiu; Hou-Duo Qi (IEEE Trans. Signal Process., 2018)

Support Vector Machine Classifier via L0/1 Soft-Margin Loss. - Huajun Wang; Yuan-Hai Shao 001; Shenglong Zhou 001; Ce Zhang; Naihua Xiu (CoRR, 2019)

Matrix Optimization Over Low-Rank Spectral Sets: Stationary Points and Local and Global Minimizers. - Xinrong Li; Naihua Xiu; Shenglong Zhou 001 (J. Optim. Theory Appl., 2020)

Robust Euclidean embedding via EDM optimization. - Shenglong Zhou 001; Naihua Xiu; Hou-Duo Qi (Math. Program. Comput., 2020)

Theoretical and numerical comparison of the Karush-Kuhn-Tucker and value function reformulations in bilevel optimization. - Alain B. Zemkoho; Shenglong Zhou 001 (Comput. Optim. Appl., 2021)

Communication-Efficient ADMM-based Federated Learning. - Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2021)

Accretionary Learning with Deep Neural Networks. - Xinyu Wei; Biing-Hwang Fred Juang; Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2021)

An extended Newton-type algorithm for ℓ2-regularized sparse logistic regression and its efficiency for classifying large-scale datasets. - Rui Wang 060; Naihua Xiu; Shenglong Zhou 001 (J. Comput. Appl. Math., 2021)

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit. - Shenglong Zhou 001; Naihua Xiu; Hou-Duo Qi (J. Mach. Learn. Res., 2021)

Newton method for ℓ0-regularized optimization. - Shenglong Zhou 001; Lili Pan 003; Naihua Xiu (Numer. Algorithms, 2021)

Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization. - Shenglong Zhou 001; Lili Pan 003; Naihua Xiu; Hou-Duo Qi (SIAM J. Optim., 2021)

Newton Hard-Thresholding Pursuit for Sparse Linear Complementarity Problem via A New Merit Function. - Shenglong Zhou 001; Meijuan Shang; Lili Pan 003; Mu Li (SIAM J. Sci. Comput., 2021)

Efficient and Convergent Federated Learning. - Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2022)

0/1 Deep Neural Networks via Block Coordinate Descent. - Hui Zhang; Shenglong Zhou 001; Geoffrey Ye Li; Naihua Xiu (CoRR, 2022)

Exact Penalty Method for Federated Learning. - Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2022)

Semismooth Newton-type method for bilevel optimization: global convergence and extensive numerical experiments. - Andreas Fischer 004; Alain B. Zemkoho; Shenglong Zhou 001 (Optim. Methods Softw., 2022)

Support Vector Machine Classifier via $L_{0/1}$L0/1 Soft-Margin Loss. - Huajun Wang; Yuan-Hai Shao 001; Shenglong Zhou 001; Ce Zhang; Naihua Xiu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Sparse SVM for Sufficient Data Reduction. - Shenglong Zhou 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Computing One-Bit Compressive Sensing via Double-Sparsity Constrained Optimization. - Shenglong Zhou 001; Ziyan Luo; Naihua Xiu; Geoffrey Ye Li (IEEE Trans. Signal Process., 2022)

Few-Shot Learning for New Environment Adaptation. - Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (GLOBECOM, 2023)

Effective Adaptation into New Environment with Few Shots: Applications to OFDM Receiver Design. - Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (MLSP, 2023)

New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming. - Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2023)

Gradient projection Newton algorithm for sparse collaborative learning using synthetic and real datasets of applications. - Jun Sun; Lingchen Kong; Shenglong Zhou 001 (J. Comput. Appl. Math., 2023)

FedGiA: An Efficient Hybrid Algorithm for Federated Learning. - Shenglong Zhou 001; Geoffrey Ye Li (IEEE Trans. Signal Process., 2023)

Federated Learning Via Inexact ADMM. - Shenglong Zhou 001; Geoffrey Ye Li (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing. - Shenglong Zhou 001; Kaidi Xu; Geoffrey Ye Li (VTC Spring, 2024)

BADM: Batch ADMM for Deep Learning. - Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (CoRR, 2024)

Fast Adaptation for Deep Learning-based Wireless Communications. - Ouya Wang; Hengtao He; Shenglong Zhou 001; Zhi Ding 001; Shi Jin 002; Khaled B. Letaief; Geoffrey Ye Li (CoRR, 2024)

Accretionary Learning With Deep Neural Networks With Applications. - Xinyu Wei; Biing-Hwang Juang; Ouya Wang; Shenglong Zhou 001; Geoffrey Ye Li (IEEE Trans. Cogn. Commun. Netw., 2024)

Federated Reinforcement Learning for Resource Allocation in V2X Networks. - Kaidi Xu; Shenglong Zhou 001; Geoffrey Ye Li (IEEE J. Sel. Top. Signal Process., 2024)

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks. - Kaidi Xu; Shenglong Zhou 001; Geoffrey Ye Li (IEEE Commun. Lett., 2024)

A step function based recursion method for 0/1 deep neural networks. - Hui Zhang; Shenglong Zhou 001; Geoffrey Ye Li; Naihua Xiu; Yiju Wang (Appl. Math. Comput., 2025)

Preconditioned Inexact Stochastic ADMM for Deep Model. - Shenglong Zhou 001; Ouya Wang; Ziyan Luo; Yongxu Zhu; Geoffrey Ye Li (CoRR, 2025)

Nonconvex Quasi-Variational Inequalities: Stability Analysis and Application to Numerical Optimization. - Joydeep Dutta; Lahoussine Lafhim; Alain B. Zemkoho; Shenglong Zhou 001 (J. Optim. Theory Appl., 2025)