Publications

A full publication list is also available on Google Scholar.

Jump to: 2022, 2015

2025

• C. Si and M. Yan, Initialization-enhanced physics-informed neural network with domain decomposition (IDPINN), Journal of Computational Physics, 530, 113914.
• J. Pan and M. Yan, Efficient sparse probability measures recovery via Bregman gradient, Journal of Scientific Computing, 102, 66.

2024

• M. Yan and Y. Li, On the improved conditions for some primal-dual algorithms, Journal of Scientific Computing, 99, 74.
• C. Wang, M. Yan, and J. Yu, Sorted L1/L2 minimization for sparse signal recovery, Journal of Scientific Computing, 99, 32.
• Z. Song, L. Shi, S. Pu, and M. Yan, Provably accelerated decentralized gradient method over unbalanced directed graphs, SIAM Journal on Optimization, 34, 1131-1156.
• Z. Song, L. Shi, S. Pu, and M. Yan, Optimal gradient tracking for decentralized optimization, Mathematical Programming, 207, 1-53.

2023

• W. Chettleburgh, Z. Huang, and M. Yan, Fast robust principal component analysis using Gauss-Newton iterations, ICASSP 2023.
• M. Hu, Y. Lou, B. Wang, M. Yan, X. Yang, and Q. Ye, Accelerated sparse recovery via gradient descent with nonlinear conjugate gradient momentum, Journal of Scientific Computing, 95, 33.

2022

• S. Alam, L. Liu, M. Yan, and M. Zhang, FedRolex: Model-heterogeneous federated learning with rolling submodel extraction, NeurIPS 2022.
• Z. Song, W. Li, K. Jin, L. Shi, M. Yan, W. Yin, K. Yuan, Communication-efficient topologies for decentralized learning with O(1) consensus rate, NeurIPS 2022.
• L. Wang and M. Yan, Hessian informed mirror descent, Journal of Scientific Computing, 92, 90.
• Z. Song, L. Shi, S. Pu, and M. Yan, Compressed gradient tracking for decentralized optimization over general directed networks, IEEE Transactions on Signal Processing, 70, 1775-1787.
• Z. Li, M. Yan, T. Zeng, and G. Zhang, Phase retrieval from incomplete data via weighted nuclear norm minimization, Pattern Recognition, 125, 108537.
• M. Yan, Asynchronous parallel computing, in W. Piegorsch, R. Levine, H. Zhang, and T. Lee (Eds.), Handbook of Computational Statistics and Data Science.

2021

• H. Tang, Y. Li, J. Liu, and M. Yan, ErrorCompensatedX: Error compensation for variance reduced algorithms, NeurIPS 2021.
• X. Zeng, M. Yan, and M. Zhang, Mercury: A framework for efficient and elastic on-device distributed DNN training, SenSys 2021, 29-41.
• S. Alghunaim, Q. Lyu, M. Yan, and A. Sayed, Dual consensus proximal algorithm for multi-agent sharing problems, IEEE Transactions on Signal Processing, 69, 5568-5579.
• H. Ouassal, M. Yan, and J. Nanzer, Decentralized frequency alignment for collaborative beamforming in distributed phased arrays, IEEE Transactions on Wireless Communications, 20, 6269-6281.
• X. Liu, W. Jin, Y. Ma, Y. Li, H. Liu, Y. Wang, M. Yan, and J. Tang, Elastic graph neural networks, ICML 2021, 6837-6849.
• S. Vakalis, D. Chen, M. Yan, and J. Nanzer, Image enhancement in active incoherent millimeter-wave imaging, Passive and Active Millimeter-Wave Imaging 2021, 1174507.
• J. Carrillo, L. Wang, W. Xu, and M. Yan, Variational asymptotic preserving scheme for the Vlasov-Poisson-Fokker-Planck system, Multiscale Modeling and Simulation, 19, 478-505.
• W. Guo, Y. Lou, J. Qin, and M. Yan, A novel regularization based on the error function for sparse recovery, Journal of Scientific Computing, 87, 31.
• Y. Li and M. Yan, On linear convergence of two decentralized algorithms, Journal of Optimization Theory and Applications, 189, 271-290.
• X. Liu, Y. Li, R. Wang, J. Tang, and M. Yan, Linear convergent decentralized optimization with compression, ICLR 2021.
• J. Liu, M. Yan, and T. Zeng, Surface-aware blind image deblurring, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1041-1055.
• Z. Li and M. Yan, A primal-dual algorithm with optimal stepsizes and its application in decentralized consensus optimization, Advances in Computational Mathematics, 47, 9.
• N. Sha, L. Shi, and M. Yan, Fast algorithms for robust principal component analysis with an upper bound on the rank, Inverse Problems and Imaging, 15, 109-128.

2020

• S. Alghunaim, M. Yan, and A. Sayed, A multi-agent primal-dual strategy for composite optimization over distributed features, EUSIPCO 2020, 2095-2099.
• C. Wang, M. Yan, Y. Rahimi, and Y. Lou, Accelerated schemes for the L1/L2 minimization, IEEE Transactions on Signal Processing, 68, 2660-2669.
• P. Chatterjee, J. Nanzer, and M. Yan, Frequency consensus for distributed antenna arrays with half-duplex wireless coordination, IEEE APS/URSI 2020.
• H. Ouassal, T. Rocco, M. Yan, and J. Nanzer, Decentralized frequency synchronization in distributed antenna arrays with quantized frequency states and directed communications, IEEE Transactions on Antennas and Propagation, 68, 5280-5288.
• X. Liu, Y. Li, J. Tang, and M. Yan, A double residual compression algorithm for efficient distributed learning, AISTATS 2020, 133-143.

2019

• H. Lyu, N. Sha, S. Qin, M. Yan, Y. Xie, and R. Wang, Manifold denoising by nonlinear robust principal component analysis, NeurIPS 2019, 13390-13400.
• J. Liu, M. Yan, J. Zeng, and T. Zeng, Image smoothing via gradient sparsity and surface area minimization, ICIP 2019, 1114-1118.
• N. Sha, M. Yan, and Y. Lin, Efficient seismic denoising techniques using robust principal component analysis, SEG 2019, 2543-2547.
• Z. Li, W. Shi, and M. Yan, A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates, IEEE Transactions on Signal Processing, 67, 4494-4506. (Code)
• Y. Hao, M. Yan, B. Heath, Y. Lei, and Y. Xie, Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares, PLOS Computational Biology, 15, e1006976.
• X. Huang, H. Yang, Y. Huang, L. Shi, F. He, A. Maier, and M. Yan, Robust mixed one-bit compressive sensing, Signal Processing, 162, 161-168.
• Z. Peng, Y. Xu, M. Yan, and W. Yin, On the convergence of asynchronous parallel iteration with unbounded delays, Journal of the Operations Research Society of China, 7, 5-42.

2018

• X. Huang, L. Shi, M. Yan, and J. A.K. Suykens, Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms, Neurocomputing, 314, 275-283. (Code)
• F. He, X. Huang, Y. Liu, and M. Yan, Fast signal recovery from saturated measurements by linear loss and nonconvex penalties, IEEE Signal Processing Letters, 25, 1374-1378.
• H. Tang, X. Lian, M. Yan, C. Zhang, and J. Liu, D2: Decentralized training over decentralized data, ICML 2018, 4848-4856.
• M. Yan, A new primal-dual algorithm for minimizing the sum of three functions with a linear operator, Journal of Scientific Computing, 76, 1698-1717. (Code)
• Y. Lou and M. Yan, Fast L1-L2 minimization via a proximal operator, Journal of Scientific Computing, 74, 767-785. (Code)
• X. Huang and M. Yan, Nonconvex penalties with analytical solutions for one-bit compressive sensing, Signal Processing, 144, 341-351.

2017

• Q. Xu, M. Yan, C. Huang, J. Xiong, Q. Huang, and Y. Yao, Exploring outliers in crowdsourced ranking for QoE, MM 2017, 1540-1548.

2016

• M. Yan and W. Yin, Self equivalence of the alternating direction method of multipliers, in R. Glowinski, S. Osher and W. Yin (Eds.), Splitting Methods in Communication and Imaging, Science and Engineering, New York, Springer, 165-194.
• I. Baytas, M. Yan, A. Jain, and J. Zhou, Asynchronous multi-task learning, ICDM 2016, 11-20.
• L. Chen, M. Yan, C. Qian, N. Xi, Z. Zhou, Y. Yang, B. Song, and L. Dong, Nonconvex compressive video sensing, Journal of Electronic Imaging, 25, 063003.
• H. Zhang, M. Yan, and W. Yin, One condition for solution uniqueness and robustness of both l1-synthesis and l1-analysis minimizations, Advances in Computational Mathematics, 42, 1381–1399.
• Z. Peng, Y. Xu, M. Yan, and W. Yin, ARock: An algorithmic framework for asynchronous parallel coordinate updates, SIAM Journal on Scientific Computing, 38, A2851-A2879.
• F. Li, S. Osher, J. Qin, and M. Yan, A multiphase image segmentation based on fuzzy membership functions and L1-norm fidelity, Journal of Scientific Computing, 69, 82-106.
• Z. Peng, T. Wu, Y. Xu, M. Yan, and W. Yin, Coordinate friendly structures, algorithms and applications, Annals of Mathematical Sciences and Applications, 1, 57-119.

2015

• X. Huang, L. Shi, and M. Yan, Nonconvex sorted L1 minimization for sparse approximation, Journal of the Operations Research Society of China, 3, 207-229.

2013

• Z. Peng, M. Yan, and W. Yin, Parallel and distributed sparse optimization, IEEE Asilomar Conference on Signals, Systems and Computers 2013 659-664.
• M. Yan, A. Bui, J. Cong, and L.A. Vese, General convergent expectation maximization (EM)-type algorithms for image reconstruction, Inverse Problems and Imaging, 7, 1007-1029.
• M. Yan, Y. Yang, and S. Osher, Exact low-rank matrix completion from sparsely corrupted entries via adaptive outlier pursuit, Journal of Scientific Computing, 56, 433-449.
• M. Yan, Restoration of images corrupted by impulse noise and mixed Gaussian impulse noise using blind inpainting, SIAM Journal on Imaging Sciences, 6, 1227-1245.
• M. Yan, Convergence analysis of SART: Optimization and statistics, International Journal of Computer Mathematics, 90, 30-47.

2012

• J. Chen, J. Cong, L. Vese, J. Villasenor, M. Yan, and Y. Zou, A hybrid architecture for compressive sensing 3D CT reconstruction, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2, 616-625.
• M. Yan, Y. Yang, and S. Osher, Robust 1-bit compressive sensing using adaptive outlier pursuit, IEEE Transactions on Signal Processing, 60, 3868-3875. (Code)
• J. Chen, J. Cong, M. Yan, and Y. Zou, FPGA-accelerated 3D reconstruction using compressive sensing, FPGA 2012, 163-166.

2011

• M. Yan, EM-type algorithms for image reconstruction with background emission and Poisson noise, International Symposium on Visual Computing 2011, 33-42.
• M. Yan, J. Chen, L. A. Vese, J. Villasenor, A. Bui, and J. Cong, EM+TV based reconstruction for cone-beam CT with reduced radiation, International Symposium on Visual Computing 2011, 1-10.
• J. Chen, M. Yan, L. A. Vese, J. Villasenor, A. Bui, and J. Cong, EM+TV for reconstruction of cone-beam CT with curved detectors using GPU, Fully3D 2011, 363-366.
• M. Yan and L. A. Vese, Expectation maximization and total variation based model for computed tomography reconstruction from undersampled data, SPIE Medical Imaging 2011, 79612X.

2008

• H. Han and M. Yan, A mixed finite element method on a staggered mesh for Navier-Stokes equations, Journal of Computational Mathematics, 26, 816-824.
• H. Han, M. Yan, and C. Wu, An energy regularization method for the backward diffusion problem and its applications to image deblurring, Communications in Computational Physics, 4, 177-194.

Thesis

• M. Yan, Image and Signal Processing with Non-Gaussian Noise: EM-Type Algorithms and Adaptive Outlier Pursuit, Department of Mathematics, University of California, Los Angeles, 2012.

Last updated: October 2025