Publications

Recent articles are also available on Google Scholar.

2023

• Z. Song, L. Shi, S. Pu, and M. Yan, Optimal gradient tracking for decentralized optimization, Mathematical Programming, accepted.

• W. Chettleburgh, Z. Huang, and M. Yan, Fast robust principle component analysis using Gauss-Newton Iterations, In: Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (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(2023), 33.

2022

• S. Alam, L. Liu, M. Yan, and M. Zhang, FedRolex: Model-heterogeneous federated learning with rolling submodel extraction, In: Proceedings of the Conference on Neural Information Processing Systems (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, In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2022).

• L. Wang and M. Yan, Hessian informed mirror descent, Journal of Scientific Computing, 92(2022), 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(2022), 1775–1787.

• Z. Li, M. Yan, T. Zeng, and G. Zhang, Phase retrieval from incomplete data via weighted nuclear norm minimization, Pattern Recognition, 125(2022), 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, In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2021).

• X. Zeng, M. Yan, and M. Zhang, Mercury: a framework for efficient and elastic on-device distributed DNN training, In: Proceedings of Conference on Embedded Networked Sensor Systems (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(2021), 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 (2021), 6269–6281.

• X. Liu, W. Jin, Y. Ma, Y. Li, H. Liu, Y. Wang, M. Yan, and J. Tang, Elastic graph neural networks, In: Proceedings of International Conference on Machine Learning (ICML 2021), PMLR 139 (2021), 6837–6849, 2021.

• S. Vakalis, D. Chen, M. Yan, and J. Nanzer, Image enhancement in active incoherent millimeter-wave imaging, In: Proceedings of Passive and Active Millimeter-Wave Imaging XXIV, 11745 (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 (2021), 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 (2021), 31.

• Y. Li and M. Yan, On linear convergence of two decentralized algorithms, Journal of Optimization Theory and Applications, 189 (2021), 271-290.

• X. Liu, Y. Li, R. Wang, J. Tang, and M. Yan, Linear convergent decentralized optimization with compression, In: Proceedings of the International Conference on Learning Representations (ICLR 2021).

• J. Liu, M. Yan, and T. Zeng, Surface-aware blind image deblurring, IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (2021), 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 (2021), 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 (2021), 109-128.

2020

• S. Alghunaim, M. Yan, and A. Sayed, A multi-agent primal-dual strategy for composite optimization over distributed features, In: Proceedings of the 28th European Signal Processing Conference (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 (2020), 2660-2669.

• P. Chatterjee, J. Nanzer, and M. Yan, Frequency consensus for distributed antenna arrays with half-duplex wireless coordination, In: Proceedings of the 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting (IEEE APS/URSI 2020), accepted.

• 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 (2020), 5280-5288.

• X. Liu, Y. Li, J. Tang, and M. Yan, A double residual compression algorithm for efficient distributed learning, In: Proceedings of the International Conference on Artificial Intelligence and Statistics (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, In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2019), 13390-13400.

• J. Liu, M. Yan, J. Zeng, and T. Zeng, Image smoothing via gradient sparsity and surface area minimization, In: Proceedings of IEEE International Conference on Image Processing (ICIP 2019), 1114-1118.

• N. Sha, M. Yan, and Y. Lin, Efficient seismic denoising techniques using robust principal component analysis, In: SEG Technical Program Expanded Abstracts (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 (2019), 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 (2019), 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 (2019), 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 (2019), 5-42. (SN SharedIt Link)

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 (2018) 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 (2018) 1374-1378.

• H. Tang, X. Lian, M. Yan, C. Zhang and J. Liu, D2: Decentralized training over decentralized data, In: Proceeding of International Conference on Machine Learning 2018, PMLR 80 (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 (2018), 1698-1717. (SN SharedIt Link) (Code)

• Y. Lou and M. Yan, Fast l1-l2 minimization via a proximal operator, Journal of Scientific Computing, 74 (2018), 767-785. (SN SharedIt Link) (Code)

• X. Huang and M. Yan, Nonconvex penalties with analytical solutions for one-bit compressive sensing, Signal Processing, 144 (2018), 341-351.

2017

• Q. Xu, M. Yan, C. Huang, J. Xiong, Q. Huang and Y. Yao, Exploring outliers in crowdsourced ranking for QoE, In: Proceedings of the ACM International Conference on Multimedia (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 (2016), New York, Springer, 165-194.

• I. Baytas, M. Yan, A. Jain and J. Zhou, Asynchronous multi-task learning, In: Proceedings of IEEE International Conference on Data Mining (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 (2016), 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 (2016), 1381–1399. (SN SharedIt Link)

• Z. Peng, Y. Xu, M. Yan and W. Yin, ARock: an algorithmic framework for asynchronous parallel coordinate updates, SIAM Journal on Scientific Computing, 38 (2016), 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 (2016), 82-106. (SN SharedIt Link)

• Z. Peng, T. Wu, Y. Xu, M. Yan and W. Yin, Coordinate friendly structures, algorithms and applications, Annals of Mathematical Sciences and Applications, 1 (2016), 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 (2015), 207-229. (SN SharedIt Link)

2013

• Z. Peng, M. Yan and W. Yin, Parallel and distributed sparse optimization, In: Proceeding of 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 (2013), 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 (2013), 433-449. (SN SharedIt Link) BibTex

• M. Yan, Restoration of images corrupted by impulse noise and mixed Gaussian impulse noise using blind inpainting, SIAM Journal on Imaging Sciences, 6 (2013), 1227-1245. BibTex

• M. Yan, Convergence analysis of SART: optimization and statistics, International Journal of Computer Mathematics, 90 (2013), 30-47. BibTex

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 (2012), 616-625. BibTex

• M. Yan, Y. Yang and S. Osher, Robust 1-bit compressive sensing using adaptive outlier pursuit IEEE Transactions on Signal Processing, 60 (2012), 3868-3875. (Code) BibTex

• J. Chen, J. Cong, M. Yan and Y. Zou, FPGA-accelerated 3D reconstruction using compressive sensing, In: Proceeding of the ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGA 2012), 163-166. BibTex

2011

• M. Yan, EM-type algorithms for image reconstruction with background emission and Poisson noise In: Proceeding of 7th International Symposium on Visual Computing, LNCS 6938 (2011), 33-42. BibTex

• 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, In: Proceeding of 7th International Symposium on Visual Computing, LNCS 6938 (2011), 1-10. BibTex

• 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, In: Proceeding of International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2011, 363-366.

• M. Yan and L. A. Vese, Expectation maximization and total variation based model for computed tomography reconstruction from undersampled data, In: Proceeding of SPIE Medical Imaging, Proceedings of SPIE, 7961 (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 (2008), 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 (2008), 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.