Research

⁺ equal contribution or alphabetical order; ^* corresponding author.

Preprints

1. Meixia Lin, Ziyang Zeng, and Yangjing Zhang^*, Multivariate regression for matrix and vector predictors: Models, theory, algorithms, and beyond, submitted, 2024.
2. Hong T. M. Chu, Meixia Lin^*, and Kim-Chuan Toh, Wasserstein distributionally robust optimization and its tractable regularization formulations, submitted, 2024. (arXiv:2402.03942)
3. Chengjing Wang, Peipei Tang^*, Wenling He, and Meixia Lin, Learning the hub graphical model with the structured sparsity via an efficient algorithm, submitted, 2024. (arXiv:2308.08852)
4. Yancheng Yuan⁺, Meixia Lin^+*, Defeng Sun, and Kim-Chuan Toh, Adaptive sieving: A dimension reduction technique for sparse optimization problems, submitted, 2023. (arXiv:2306.17369)

Publications in Refereed Scientific Journals

1. 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)
2. 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)
3. Meixia Lin⁺, Yancheng Yuan⁺, Defeng Sun^*, and Kim-Chuan Toh, A highly efficient algorithm for solving exclusive lasso problems, Optimization Methods and Software, 2023, pp. 1–30. (arXiv:2306.14196)
4. 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)
5. 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)
6. 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

1. 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)
2. 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) (arXiv:2112.06007)