Research
+ equal contribution or alphabetical order; * corresponding author.
Preprints
- Lei Yang, Liwei Luo, and Meixia Lin*, Shadow-point enhanced inexact accelerated proximal gradient method with preserved convergence guarantees, submitted, April 2025.
- Meixia Lin, and Yangjing Zhang*, Low rank convex clustering for matrix-valued observations, submitted, December 2024. (arXiv:2412.17328)
- Hong T. M. Chu, Meixia Lin*, and Kim-Chuan Toh, Wasserstein distributionally robust optimization and its tractable regularization formulations, submitted, May 2024. (arXiv:2402.03942)
Publications in Refereed Scientific Journals
- Chengjing Wang, Peipei Tang*, Wenling He, and Meixia Lin, Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm, Journal of Scientific Computing, 2025, to appear. (arXiv:2308.08852)
- Yancheng Yuan, Meixia Lin*, Defeng Sun, and Kim-Chuan Toh, Adaptive sieving: A dimension reduction technique for sparse optimization problems, Mathematical Programming Computation, 2025. [code] (arXiv:2306.17369)
- Meixia Lin, Ziyang Zeng, and Yangjing Zhang*, Multiple regression for matrix and vector predictors: Models, theory, algorithms, and beyond, Electronic Journal of Statistics, 2024, Vol. 18(2), pp. 5563–5600. [code] (arXiv:2410.19264)
- Meixia Lin, Defeng Sun, Kim-Chuan Toh, and Chengjing Wang*, Estimation of sparse Gaussian graphical models with hidden clustering structure, Journal of Machine Learning Research, 2024, Vol. 25(256), pp. 1–36. (arXiv: 2004.08115)
- Zhenning Cai, Bo Lin*, and Meixia Lin, A positive and moment-preserving Fourier spectral method, SIAM Journal on Numerical Analysis, 2024, Vol. 62(1), pp. 273–294. (arXiv:2304.11847)
- Meixia Lin+, Yancheng Yuan+, Defeng Sun*, and Kim-Chuan Toh, A highly efficient algorithm for solving exclusive lasso problems, Optimization Methods and Software, 2024, Vol. 39(3), pp. 489–518. [code] (arXiv:2306.14196)
- Subhroshekhar Ghosh, Meixia Lin*, and Dongfang Sun, Signal analysis via the stochastic geometry of spectrogram level sets, IEEE Transactions on Signal Processing, 2022, Vol. 70, pp. 1104–1117. (arXiv:2105.02471)
- Meixia Lin*, Defeng Sun, and Kim-Chuan Toh, An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems, Mathematical Programming Computation, 2022, Vol. 14, pp. 223-270. [code] (arXiv:2012.04862)
- Meixia Lin, Yong-Jin Liu*, Defeng Sun, and Kim-Chuan Toh, Efficient sparse semismooth Newton methods for the clustered lasso problem, SIAM Journal on Optimization, 2019, Vol. 29(3), pp. 2026–2052. (arXiv:1808.07181)
Publications in Conference Proceedings
- Meixia Lin, and Yangjing Zhang*, DNNLasso: Scalable graph learning for matrix-variate data, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. [code] (arXiv:2403.02608)
- Rémi Bardenet+, Subhroshekhar Ghosh+*, and Meixia Lin+*, Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD, Conference on Neural Information Processing Systems (NeurIPS), 2021. (Spotlight presentation, less than 3% acceptance rate) [code] (arXiv:2112.06007)