Research

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

Preprints

1. Yancheng Yuan⁺, Meixia Lin⁺, Defeng Sun^*, and Kim-Chuan Toh, Adaptive sieving: A dimension reduction technique for sparse optimization problems, 2023.
2. Zhenning Cai, Bo Lin^*, and Meixia Lin, A positive and moment-preserving Fourier spectral method, 2023.
3. Meixia Lin, Yancheng Yuan^*, Defeng Sun, and Kim-Chuan Toh, Adaptive sieving with PPDNA: Generating solution paths of exclusive lasso models, 2020. (arXiv: 2009.08719)
4. Meixia Lin, Defeng Sun, Kim-Chuan Toh, and Chengjing Wang^*, Estimation of sparse Gaussian graphical models with hidden clustering structure, 2020. (arXiv: 2004.08115)

Publications

1. Meixia Lin⁺, Yancheng Yuan⁺, Defeng Sun^*, and Kim-Chuan Toh, A Highly Efficient Algorithm for Solving Exclusive Lasso Problems, to appear in Optimization Methods and Software, 2023.
2. Meixia Lin^*, Defeng Sun, and Kim-Chuan Toh, An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems, Mathematical Programming Computation, 14.2 (2022): 223-270. [code]
3. Subhroshekhar Ghosh, Meixia Lin^*, and Dongfang Sun, Signal analysis via the stochastic geometry of spectrogram level sets, IEEE Transactions on Signal Processing, 70 (2022), pp. 1104–1117.
4. Rémi Bardenet⁺, Subhroshekhar Ghosh^+*, and Meixia Lin^+*, Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD, in Conference on Neural Information Processing Systems (NeurIPS), 2021. (Spotlight presentation, less than 3% acceptance rate)
5. 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, 29 (2019), pp. 2026–2052.