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 | 36 | 2019 |
An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems M Lin, D Sun, KC Toh Mathematical Programming Computation, 2021 | 19* | 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 | 10 | 2022 |
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 | 9* | 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 | 7 | 2021 |
Estimation of sparse Gaussian graphical models with hidden clustering structure M Lin, D Sun, KC Toh, C Wang arXiv preprint arXiv:2004.08115, 2020 | 3 | 2020 |
A positive and moment-preserving Fourier spectral method Z Cai, B Lin, M Lin SIAM Journal on Numerical Analysis 62 (1), 273-294, 2024 | 2 | 2024 |
Adaptive sieving: A dimension reduction technique for sparse optimization problems Y Yuan, M Lin, D Sun, KC Toh arXiv preprint arXiv:2306.17369, 2023 | 2 | 2023 |
A highly efficient algorithm for solving exclusive lasso problems M Lin, Y Yuan, D Sun, KC Toh Optimization Methods and Software, 1-30, 2023 | 1 | 2023 |
Wasserstein distributionally robust optimization and its tractable regularization formulations H Chu, M Lin, KC Toh arXiv preprint arXiv:2402.03942, 2024 | | 2024 |
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 |