Jiaxi Ying

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 35

Top Topics

Bipartite Graph

Financial Markets

Top Venues

IEEE Trans. Signal Process.

IEEE Wirel. Commun. Lett.

Publications

Jian-Feng Cai, Yu Long, Ruixue Wen, Jiaxi Ying
A Fast and Provable Algorithm for Sparse Phase Retrieval. ICLR (2024)
Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals. CoRR (2024)
Jian-Feng Cai, Yu Long, Ruixue Wen, Jiaxi Ying
A Fast and Provable Algorithm for Sparse Phase Retrieval. CoRR (2023)
Xiwen Wang, Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-$t$ Distribution. IEEE Trans. Signal Process. 71 (2023)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Sandeep Kumar, Daniel P. Palomar
Estimating Normalized Graph Laplacians in Financial Markets. ICASSP (2023)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Adaptive Estimation of Graphical Models under Total Positivity. ICML (2023)
Xi Han, Jiaxi Ying, An Liu, Li Ma
A nested tensor-based receiver employing triple constellation precoding for three-hop cooperative communication systems. Digit. Signal Process. 133 (2023)
Xiwen Wang, Jiaxi Ying, Daniel P. Palomar
Gaussian Graphical Models via Bridge-Block Decomposition. CoRR (2023)
Jiaxi Ying, Xi Han, Rui Zhou, Xiwen Wang, Hing Cheung So
Network Topology Inference with Sparsity and Laplacian Constraints. CoRR (2023)
Xiwen Wang, Jiaxi Ying, Daniel P. Palomar
Gaussian Graphical Models via Bridge-Block Decomposition. NeurIPS (2023)
Jianfeng Cai, José Vinícius de Miranda Cardoso, Daniel P. Palomar, Jiaxi Ying
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity. NeurIPS (2023)
Xiwen Wang, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Efficient Algorithms for General Isotone Optimization. AAAI (2022)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar
Learning Bipartite Graphs: Heavy Tails and Multiple Components. NeurIPS (2022)
Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations. IEEE Trans. Signal Process. 70 (2022)
Xi Han, Xinyuan Zhao, Jiaxi Ying, Feifei Gao
Tensor-Based Information Monitoring Receiver in UAV-Aided MIMO Communication Systems. IEEE Wirel. Commun. Lett. 11 (1) (2022)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Graphical Models. CoRR (2022)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields. ACSCC (2021)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Jian-Feng Cai, Daniel P. Palomar
Fast Projected Newton-like Method for Precision Matrix Estimation with Nonnegative Partial Correlations. CoRR (2021)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Minimax Estimation of Laplacian Constrained Precision Matrices. AISTATS (2021)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar
Graphical Models in Heavy-Tailed Markets. NeurIPS (2021)
Xi Han, Yuyu Zhao, Jiaxi Ying
Semi-Blind Receivers for UAV M-KRST Coding MIMO Systems Based on Nested Tensor Models. IEEE Wirel. Commun. Lett. 10 (1) (2021)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel Pérez Palomar
Algorithms for Learning Graphs in Financial Markets. CoRR (2020)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Unified Framework for Structured Graph Learning via Spectral Constraints. J. Mach. Learn. Res. 21 (2020)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. NeurIPS (2020)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? CoRR (2020)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Unified Framework for Structured Graph Learning via Spectral Constraints. CoRR (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Structured Graph Learning Via Laplacian Spectral Constraints. NeurIPS (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. ACSSC (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Structured Graph Learning Via Laplacian Spectral Constraints. CoRR (2019)
Hengfa Lu, Xinlin Zhang, Tianyu Qiu, Jian Yang, Jiaxi Ying, Di Guo, Zhong Chen, Xiaobo Qu
Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy. IEEE Trans. Biomed. Eng. 65 (4) (2018)
Jiaxi Ying, Jian-Feng Cai, Di Guo, Gongguo Tang, Zhong Chen, Xiaobo Qu
Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery - Application in Fast NMR Spectroscopy. IEEE Trans. Signal Process. 66 (21) (2018)
Hong Zheng, Kun Zeng, Di Guo, Jiaxi Ying, Yu Yang, Xi Peng, Feng Huang, Zhong Chen, Xiaobo Qu
Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement. IEEE Access 6 (2018)
Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu
Hankel Matrix Nuclear Norm Regularized Tensor Completion for N-dimensional Exponential Signals. IEEE Trans. Signal Process. 65 (14) (2017)
Xiaobo Qu, Jiaxi Ying, Jian-Feng Cai, Zhong Chen
Accelerated magnetic resonance spectroscopy with Vandermonde factorization. EMBC (2017)
Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu
Hankel Matrix Nuclear Norm Regularized Tensor Completion for N-dimensional Exponential Signals. CoRR (2016)