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Meixia LIN
Meixia LIN
Assistant Professor, Engineering Systems and Design, Singapore University of Technology and Design
Verified email at u.nus.edu - Homepage
Title
Cited by
Cited by
Year
Efficient sparse semismooth Newton methods for the clustered Lasso problem
M Lin, YJ Liu, D Sun, KC Toh
SIAM Journal on Optimization 29 (3), 2026-2052, 2019
29*2019
An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems
M Lin, D Sun, KC Toh
Mathematical Programming Computation, 2021
17*2021
Signal analysis via the stochastic geometry of spectrogram level sets
S Ghosh, M Lin, D Sun
IEEE Transactions on Signal Processing 70, 1104-1117, 2022
82022
Adaptive sieving with PPDNA: Generating solution paths of exclusive lasso models
M Lin, Y Yuan, D Sun, KC Toh
arXiv preprint arXiv:2009.08719, 2020
8*2020
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
R Bardenet, S Ghosh, M Lin
Advances in Neural Information Processing Systems 34, 16226-16237, 2021
32021
Estimation of sparse Gaussian graphical models with hidden clustering structure
M Lin, D Sun, KC Toh, C Wang
arXiv preprint arXiv:2004.08115, 2020
22020
Adaptive sieving: A dimension reduction technique for sparse optimization problems
Y Yuan, M Lin, D Sun, KC Toh
arXiv preprint arXiv:2306.17369, 2023
12023
A Highly Efficient Algorithm for Solving Exclusive Lasso Problems
M Lin, Y Yuan, D Sun, KC Toh
arXiv preprint arXiv:2306.14196, 2023
12023
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
C Wang, P Tang, W He, M Lin
arXiv preprint arXiv:2308.08852, 2023
2023
A positive and moment-preserving Fourier spectral method
Z Cai, B Lin, M Lin
arXiv preprint arXiv:2304.11847, 2023
2023
Estimation and inference of signals via the stochastic geometry of spectrogram level sets
S Ghosh, M Lin, D Sun
arXiv e-prints, arXiv: 2105.02471, 2021
2021
Supplementary material to Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
R Bardenet, S Ghosh, M Lin
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