Accelerated magnetic resonance spectroscopy with Vandermonde factorization. - Xiaobo Qu 001; Jiaxi Ying; Jian-Feng Cai 001; Zhong Chen 005 (EMBC, 2017)

Hankel Matrix Nuclear Norm Regularized Tensor Completion for N-dimensional Exponential Signals. - Jiaxi Ying; Hengfa Lu; Qingtao Wei; Jian-Feng Cai 001; Di Guo; Jihui Wu; Zhong Chen 005; Xiaobo Qu 001 (IEEE Trans. Signal Process., 2017)

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement. - Hong Zheng; Kun Zeng; Di Guo; Jiaxi Ying; Yu Yang 002; Xi Peng 004; Feng Huang; Zhong Chen 005; Xiaobo Qu 001 (IEEE Access, 2018)

Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy. - Hengfa Lu; Xinlin Zhang; Tianyu Qiu; Jian Yang; Jiaxi Ying; Di Guo; Zhong Chen 005; Xiaobo Qu 001 (IEEE Trans. Biomed. Eng., 2018)

Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery - Application in Fast NMR Spectroscopy. - Jiaxi Ying; Jian-Feng Cai 001; Di Guo; Gongguo Tang; Zhong Chen 005; Xiaobo Qu 001 (IEEE Trans. Signal Process., 2018)

Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (ACSSC, 2019)

Structured Graph Learning Via Laplacian Spectral Constraints. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (NeurIPS, 2019)

Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (NeurIPS, 2020)

Does the 𝓁1-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (CoRR, 2020)

Algorithms for Learning Graphs in Financial Markets. - José Vinícius de Miranda Cardoso; Jiaxi Ying; Daniel Pérez Palomar (CoRR, 2020)

A Unified Framework for Structured Graph Learning via Spectral Constraints. - Sandeep Kumar 005; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (J. Mach. Learn. Res., 2020)

A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (ACSCC, 2021)

Minimax Estimation of Laplacian Constrained Precision Matrices. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (AISTATS, 2021)

Graphical Models in Heavy-Tailed Markets. - José Vinícius de Miranda Cardoso; Jiaxi Ying; Daniel P. Palomar (NeurIPS, 2021)

Fast Projected Newton-like Method for Precision Matrix Estimation with Nonnegative Partial Correlations. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Jian-Feng Cai 001; Daniel P. Palomar (CoRR, 2021)

Semi-Blind Receivers for UAV M-KRST Coding MIMO Systems Based on Nested Tensor Models. - Xi Han; Yuyu Zhao; Jiaxi Ying (IEEE Wirel. Commun. Lett., 2021)

Efficient Algorithms for General Isotone Optimization. - Xiwen Wang 003; Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (AAAI, 2022)

Learning Bipartite Graphs: Heavy Tails and Multiple Components. - José Vinícius de Miranda Cardoso; Jiaxi Ying; Daniel P. Palomar (NeurIPS, 2022)

Adaptive Estimation of MTP2 Graphical Models. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (CoRR, 2022)

Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations. - Rui Zhou; Jiaxi Ying; Daniel P. Palomar (IEEE Trans. Signal Process., 2022)

Tensor-Based Information Monitoring Receiver in UAV-Aided MIMO Communication Systems. - Xi Han; Xinyuan Zhao; Jiaxi Ying; Feifei Gao (IEEE Wirel. Commun. Lett., 2022)

Estimating Normalized Graph Laplacians in Financial Markets. - José Vinícius de Miranda Cardoso; Jiaxi Ying; Sandeep Kumar 005; Daniel P. Palomar (ICASSP, 2023)

Adaptive Estimation of Graphical Models under Total Positivity. - Jiaxi Ying; José Vinícius de Miranda Cardoso; Daniel P. Palomar (ICML, 2023)

Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity. - Jianfeng Cai 001; José Vinícius de Miranda Cardoso; Daniel P. Palomar; Jiaxi Ying (NeurIPS, 2023)

Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition. - Xiwen Wang 003; Jiaxi Ying; Daniel P. Palomar (NeurIPS, 2023)

Network Topology Inference with Sparsity and Laplacian Constraints. - Jiaxi Ying; Xi Han; Rui Zhou; Xiwen Wang 003; Hing Cheung So (CoRR, 2023)

Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-$t$ Distribution. - Xiwen Wang 003; Rui Zhou; Jiaxi Ying; Daniel P. Palomar (IEEE Trans. Signal Process., 2023)

A nested tensor-based receiver employing triple constellation precoding for three-hop cooperative communication systems. - Xi Han; Jiaxi Ying; An Liu 002; Li Ma (Digit. Signal Process., 2023)

A Fast and Provable Algorithm for Sparse Phase Retrieval. - Jian-Feng Cai 001; Yu Long; Ruixue Wen; Jiaxi Ying (ICLR, 2024)

Adaptive Passive-Aggressive Framework for Online Regression with Side Information. - Runhao Shi; Jiaxi Ying; Daniel P. Palomar (NeurIPS, 2024)

Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals. - Andrei Buciulea; Jiaxi Ying; Antonio G. Marques; Daniel P. Palomar (IEEE Trans. Signal Process., 2025)

Time-Varying Graph Learning for Data With Heavy-Tailed Distribution. - Amirhossein Javaheri; Jiaxi Ying; Daniel P. Palomar; Farokh Marvasti (IEEE Trans. Signal Process., 2025)