| Hankel Matrix Nuclear Norm Regularized Tensor Completion for -dimensional Exponential Signals 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 | 109 | 2017 |
| 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 | 101 | 2020 |
| 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 | 80 | 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 | 58 | 2019 |
| 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 | 52 | 2018 |
| 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 | 51* | 2020 |
| 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 | 36 | 2017 |
| 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 | 20 | 2021 |
| Does the -norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? J Ying, JVM Cardoso, DP Palomar arXiv preprint arXiv:2006.14925, 2020 | 18* | 2020 |
| Graphical models in heavy-tailed markets JV de Miranda Cardoso, J Ying, D Palomar Advances in Neural Information Processing Systems 34, 19989-20001, 2021 | 17 | 2021 |
| Algorithms for learning graphs in financial markets JVM Cardoso, J Ying, DP Palomar arXiv preprint arXiv:2012.15410, 2020 | 12 | 2020 |
| 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 | 9 | 2018 |
| 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 | 6 | 2022 |
| Adaptive Estimation of Graphical Models under Total Positivity J Ying, JVDM Cardoso, DP Palomar International Conference on Machine Learning, 40054-40074, 2023 | 5* | 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 | 5 | 2021 |
| 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 | 4 | 2022 |
| 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 | 4 | 2020 |
| Accelerated magnetic resonance spectroscopy with Vandermonde factorization X Qu, J Ying, JF Cai, Z Chen 2017 39th Annual International Conference of the IEEE Engineering in …, 2017 | 4 | 2017 |
| 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 | 3 | 2021 |
| Bipartite structured Gaussian graphical modeling via adjacency spectral priors S Kumar, J Ying, JVM Cardoso, DP Palomar 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 322-326, 2019 | 3 | 2019 |
Daniel P. PalomarDepartment of Electronic & Computer Engineering, Department of Industrial Eng. & Decision AnalyticsVerified email at ust.hk
