[1] S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, and P. Zwiernik, “Total positivity in Markov structures,” The Annals of Statistics, vol. 45, no. 3, pp. 1152–1184, 2017.
[2] M. Slawski and M. Hein, "Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov random fields," Linear Algebra and its Applications, vol. 473, pp. 145-179, 2015.
[3] H. E. Egilmez, E. Perez, and A. Ortega, "Graph learning from data under Laplacian and structural constraints," IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 6, pp. 825-841, 2017.
[4] S. Lauritzen, C. Uhler, and P. Zwiernik, “Maximum likelihood estimation in Gaussian models under total positivity,” The Annals of Statistics, vol. 47, no. 4, pp. 1835–1863, 2019.
[5] R. Agrawal, U. Roy, and C. Uhler, "Covariance Matrix Estimation under Total Positivity for Portfolio Selection," Journal of Financial Econometrics, vol. 20, no. 2, pp. 367-389, 09 2020.
[6] S. Lauritzen, C. Uhler, and P. Zwiernik, "Total positivity in exponential families with application to binary variables," The Annals of Statistics, vol. 49, no. 3, pp. 1436-1459, 2021.
[7] E. Perez, H. E. Egilmez, and A. Ortega, "Learning graphs with monotone topology properties and multiple connected components," IEEE Transactions on Signal Processing, vol. 66, no. 9, pp. 2399-2413, 2018.
[8] E. Perez and A. Ortega, "Generalized Laplacian precision matrix estimation for graph signal processing," in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 6350-6354.
[9] Z. Deng and A. M.-C. So, “A fast proximal point algorithm for generalized graph laplacian learning,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 5425-5429.
[10] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, "The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains," IEEE Signal Processing Magazine, vol. 30, no. 3, pp. 83-98, 2013.
[11] J. Kelner, F. Koehler, R. Meka, and A. Moitra, “Learning some popular Gaussian graphical models without condition number bounds,” in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 10986–10998.
[12] Y. Wang, U. Roy, and C. Uhler, “Learning high-dimensional Gaussian graphical models under total positivity without adjustment of tuning parameters,” in International Conference on Artificial Intelligence and Statistics, vol. 108, 2020, pp. 2698–2708.
[13] J. Ying, J. V. d. M. Cardoso, and D. P. Palomar, "Adaptive estimation of graphical models under total positivity," in International Conference on Machine Learning, 2023, pp. 40054-40074.
[14] S. Kumar, J. Ying, J. V. d. M. Cardoso, and D. P. Palomar, “A unified framework for structured graph learning via spectral constraints,” Journal of Machine Learning Research, vol. 21, no. 22, pp. 1–60, 2020.
[15] J. V. d. M. Cardoso, J. Ying, and D. P. Palomar, “Learning bipartite graphs: Heavy tails and multiple components,” in Advances in Neural Information Processing Systems, vol. 35, 2022, pp. 14044–14057.
[16] S. Kumar, J. Ying, J. V. d. M. Cardoso, and D. P. Palomar, “Structured graph learning via Laplacian spectral constraints,” in Advances in Neural Information Processing Systems, 2019, pp. 11-647-11-658.
[17] O. Banerjee, L. E. Ghaoui, and A. d'Aspremont, "Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data," Journal of Machine Learning Research, vol. 9, no. Mar, pp. 485-516, 2008.
[18] A. d'Aspremont, O. Banerjee, and L. El Ghaoui, "First-order methods for sparse covariance selection," SIAM Journal on Matrix Analysis and Applications, vol. 30, no. 1, pp. 56-66, 2008.