A unified framework for structured graph learning via spectral constraints S Kumar, J Ying, JVM Cardoso, DP Palomar
Journal of Machine Learning Research 21 (22), 1-60, 2020
134 2020 Hankel Matrix Nuclear Norm Regularized Tensor Completion for J Ying, H Lu, Q Wei, JF Cai, D Guo, J Wu, Z Chen, X Qu
IEEE Transactions on Signal Processing 65 (14), 3702-3717, 2017
117 2017 Vandermonde factorization of Hankel matrix for complex exponential signal recovery—Application in fast NMR spectroscopy J Ying, JF Cai, D Guo, G Tang, Z Chen, X Qu
IEEE Transactions on Signal Processing 66 (21), 5520-5533, 2018
86 2018 Structured graph learning via Laplacian spectral constraints S Kumar, J Ying, JV de Miranda Cardoso, D Palomar
Advances in neural information processing systems 32, 2019
75 2019 Nonconvex sparse graph learning under Laplacian constrained graphical model J Ying, JVM Cardoso, DP Palomar
Advances in Neural Information Processing Systems 33, 7101-7113, 2020
64 2020 Multi-contrast brain MRI image super-resolution with gradient-guided edge enhancement H Zheng, K Zeng, D Guo, J Ying, Y Yang, X Peng, F Huang, Z Chen, X Qu
IEEE access 6, 57856-57867, 2018
57 2018 Low rank enhanced matrix recovery of hybrid time and frequency data in fast magnetic resonance spectroscopy H Lu, X Zhang, T Qiu, J Yang, J Ying, D Guo, Z Chen, X Qu
IEEE Transactions on Biomedical Engineering 65 (4), 809-820, 2017
38 2017 Graphical models in heavy-tailed markets JV de Miranda Cardoso, J Ying, D Palomar
Advances in Neural Information Processing Systems 34, 19989-20001, 2021
32 2021 Minimax estimation of Laplacian constrained precision matrices J Ying, JV de Miranda Cardoso, D Palomar
International Conference on Artificial Intelligence and Statistics, 3736-3744, 2021
28 2021 Does the J Ying, JVM Cardoso, DP Palomar
arXiv preprint arXiv:2006.14925, 2020
24 * 2020 Algorithms for learning graphs in financial markets JVM Cardoso, J Ying, DP Palomar
arXiv preprint arXiv:2012.15410, 2020
17 2020 Covariance matrix estimation under low-rank factor model with nonnegative correlations R Zhou, J Ying, DP Palomar
IEEE Transactions on Signal Processing 70, 4020-4030, 2022
14 2022 Learning bipartite graphs: Heavy tails and multiple components JV de Miranda Cardoso, J Ying, D Palomar
Advances in Neural Information Processing Systems 35, 14044-14057, 2022
12 2022 High-fidelity spectroscopy reconstruction in accelerated NMR X Qu, T Qiu, D Guo, H Lu, J Ying, M Shen, B Hu, V Orekhov, Z Chen
Chemical communications 54 (78), 10958-10961, 2018
11 2018 Tensor-based information monitoring receiver in UAV-aided MIMO communication systems X Han, X Zhao, J Ying, F Gao
IEEE Wireless Communications Letters 11 (1), 155-159, 2021
8 2021 Adaptive Estimation of Graphical Models under Total Positivity J Ying, JVDM Cardoso, DP Palomar
International Conference on Machine Learning, 40054-40074, 2023
7 * 2023 Semi-blind receivers for UAV M-KRST coding MIMO systems based on nested tensor models X Han, Y Zhao, J Ying
IEEE Wireless Communications Letters 10 (1), 185-188, 2020
7 2020 Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity JF Cai, JVDM Cardoso, DP Palomar, J Ying
Thirty-seventh Conference on Neural Information Processing Systems, 2023
6 * 2023 A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields J Ying, JVM Cardoso, DP Palomar
2021 55th Asilomar Conference on Signals, Systems, and Computers, 1520-1524, 2021
6 2021 Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals A Buciulea, J Ying, AG Marques, DP Palomar
arXiv preprint arXiv:2404.02621, 2024
5 2024 
Daniel P. PalomarDepartment of Electronic & Computer Engineering, Department of Industrial Eng. & Decision AnalyticsAdresse e-mail validée de ust.hk
