News

Jan 2024: A paper is accepted in ICLR 2024.

Sep 2023: Two papers are accepted in NeurIPS 2023.

Aug 2023: A paper is accepted in IEEE Trans. on Signal Processing.

Jul 2023: Invited talk at IEEE ICICN 2023: Inferring Network Topologies: Modelling, Algorithms, and Theory.

Apr 2023: A paper is accepted in ICML 2023.

Publications

Articles distinguished by "with ..." have alphabetical author lists;   * indicates the corresponding author.

Preprints

• Does the \(\ell_1\)-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?
  J. Ying, J.V.M. Cardoso, and D.P. Palomar, preprint. [paper]

Selected Journal and Conference Papers

• A Fast and Provable Algorithm for Sparse Phase Retrieval
  J. Ying* with J.-F. Cai, Y. Long, and R. Wen, accepted in International Conference on Learning Representations (ICLR), 2024. [paper]

• Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity
  J. Ying* with J.-F. Cai, J.V.M. Cardoso, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2023. [paper]

• Learning Large-Scale MTP\(_2\) Gaussian Graphical Models via Bridge-Block Decomposition
  X. Wang, J. Ying*, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2023. [paper]

• Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution
  X. Wang, R. Zhou, J. Ying, and D.P. Palomar, IEEE Transactions on Signal Processing, 2023. [paper]

• Adaptive Estimation of Graphical Models under Total Positivity
  J. Ying*, J.V.M. Cardoso, and D.P. Palomar, International Conference on Machine Learning (ICML), 2023. [paper]

• Learning Bipartite Graphs: Heavy Tails and Multiple Components
  J.V.M. Cardoso, J. Ying, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2022. [paper]

• Covariance Matrix Estimation Under Low-Rank Factor Model with Nonnegative Correlations
  R. Zhou, J. Ying*, and D.P. Palomar, IEEE Transactions on Signal Processing, 2022. [paper]

• Tensor-Based Information Monitoring Receiver in UAV-Aided MIMO Communication Systems
  X. Han, X. Zhao, J. Ying, and F. Gao, IEEE Wireless Communications Letters, 2022. [paper]

• Efficient Algorithms for General Isotone Optimization
  X. Wang, J. Ying, J.V.M. Cardoso, and D.P. Palomar, The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. [paper]

• Minimax Estimation of Laplacian Constrained Precision Matrices
  J. Ying, J.V.M. Cardoso, and D.P. Palomar, International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. [paper]

• Graphical Models for Heavy-Tailed Markets
  J.V.M. Cardoso, J. Ying, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2021. [paper]

• Semi-Blind Receivers for UAV M-KRST Coding MIMO Systems Based on Nested Tensor Models
  X. Han, Y. Zhao and, J. Ying, IEEE Wireless Communications Letters, 2021. [paper]

• Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
  J. Ying, J.V.M. Cardoso, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2020. [paper]

• A Unified Framework For Structured Graph Learning Via Spectral Constraints
  S. Kumar, J. Ying*, J.V.M. Cardoso, and D.P. Palomar, Journal of Machine Learning Research, 2020. [paper]

• Structured Graph Learning Via Laplacian Spectral Constraints
  S. Kumar, J. Ying, J.V.M. Cardoso, and D.P. Palomar, Advances in Neural Information Processing Systems (NeurIPS), 2019. [paper]

• Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery - Application in Fast NMR Spectroscopy
  J. Ying, J.-F. Cai, D. Guo, G. Tang, Z. Chen, and X. Qu, IEEE Transactions on Signal Processing, 2018. [paper]

• Hankel Matrix Nuclear Norm Regularized Tensor Completion for \(N\)-dimensional Exponential Signals
  J. Ying, H. Lu, Q. Wei, J.-F. Cai, D. Guo, J. Wu, Z. Chen, and X. Qu, IEEE Transactions on Signal Processing, 2017. [paper]

Book Chapters and Other Conference Papers

• Nonconvex Graph Learning: Sparsity, Heavy-tails, and Clustering
  J.V.M. Cardoso, J. Ying, and D.P. Palomar, Signal Processing and Machine Learning Theory, Academic Press, 2024. [paper]

• Network Topology Inference with Sparsity and Laplacian Constraints
  J. Ying, X. Han, R. Zhou, X. Wang, H.C. So, IEEE International Conference on Information, Communication and Networks (ICICN), 2023. [paper]

• Estimating Normalized Graph Laplacians in Financial Markets
  J.V.M. Cardoso, J. Ying, and D.P. Palomar, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. [paper]

• A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields
  J. Ying, J.V.M. Cardoso, and D.P. Palomar, 55th Asilomar Conference on Signals, Systems and Computers (Asilomar), 2021. [paper]

• Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors
  S. Kumar, J. Ying, J.V.M. Cardoso, and D.P. Palomar, 53rd Asilomar Conference on Signals, Systems and Computers (Asilomar), 2019. [paper]

• Accelerated Magnetic Resonance Spectroscopy with Vandermonde Factorization
  X. Qu, J. Ying, J.-F. Cai, Z. Chen, 39th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. [paper]