2023 NeurIPS Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity J.-F., Cai, Cardoso, J. V. M., Palomar, D. P., and Ying, J. In Advances in Neural Information Processing Systems 2023 HTML PDF Code ICML Adaptive Estimation of Graphical Models under Total Positivity Ying, J., Cardoso, J. V. M., and Palomar, D. P. In International Conference on Machine Learning 2023 HTML PDF Code ICASSP Estimating Normalized Graph Laplacians in Financial Markets Cardoso, J. V. M., Ying, J., Kumar, S., and Palomar, D. P. In International Conference on Acoustics, Speech, and Signal Processing 2023 HTML PDF 2022 NeurIPS Learning Bipartite Graphs: Heavy Tails and Multiple Components Cardoso, J. V. M., Ying, J., and Palomar, D. P. In Advances in Neural Information Processing Systems 2022 HTML PDF Code AAAI Efficient Algorithms for General Isotone Optimization Wang, X., Ying, J., Cardoso, J. V. M., and Palomar, D. P. In The Thirty-Sixth AAAI Conference on Artificial Intelligence 2022 HTML PDF 2021 Asilomar A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields Ying, J., Cardoso, J. V. M., and Palomar, D. P. In 2021 55th Asilomar Conference on Signals, Systems, and Computers 2021 HTML PDF arXiv Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity Cai, J-F., Cardoso, J. V. M., Palomar, D. P., and Ying, J. In arXiv e-prints 2021 HTML PDF Code NeurIPS Graphical Models in Heavy-Tailed Markets Cardoso, J. V. M., Ying, J., and Palomar, D. P. In Advances in Neural Information Processing Systems 2021 HTML PDF Code AISTATS Minimax Estimation of Laplacian Constrained Precision Matrices Ying, J., Cardoso, J. V. M., and Palomar, D. P. In 24th International Conference on Artificial Intelligence and Statistics 2021 HTML PDF Code 2020 arXiv Algorithms for Learning Graphs in Financial Markets Cardoso, J. V. M., Ying, J., and Palomar, D. P. In arXiv e-prints 2020 HTML PDF Code Asilomar Learning Undirected Graphs in Financial Markets Cardoso, J. V. M., and Palomar, D. P. In 2020 54th Asilomar Conference on Signals, Systems, and Computers 2020 HTML PDF arXiv Does the L1 norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? Ying, J., Cardoso, J. V. M., and Palomar, D. P. In arXiV e-prints 2020 HTML PDF Code NeurIPS Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model Ying, J., Cardoso, J. V. M., and Palomar, D. P. In Advances in Neural Information Processing Systems 2020 HTML PDF Code JMLR A Unified Framework for Structured Graph Learning via Spectral Constraints Kumar, S., Ying, J., Cardoso, J. V. M., and Palomar, D. P. Journal of Machine Learning Research 2020 HTML PDF Code 2019 NeurIPS Structured Graph Learning Via Laplacian Spectral Constraints Kumar, S., Ying, J., Cardoso, J. V. M., and Palomar, D. P. In Advances in Neural Information Processing Systems 2019 HTML PDF Code