Ying, Jiaxi

13 publications

ICML 2025 Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity Jian-Feng Cai, Zhuozhi Xian, Jiaxi Ying

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

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

ICML 2023 Adaptive Estimation of Graphical Models Under Total Positivity Jiaxi Ying, José Vinı́cius De Miranda Cardoso, Daniel P. Palomar

NeurIPS 2023 Fast Projected Newton-like Method for Precision Matrix Estimation Under Total Positivity Jian-Feng Cai, José Vinícius de Miranda Cardoso, Daniel Palomar, Jiaxi Ying

NeurIPS 2023 Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition Xiwen Wang, Jiaxi Ying, Daniel Palomar

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

NeurIPS 2022 Learning Bipartite Graphs: Heavy Tails and Multiple Components José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar

AISTATS 2021 Minimax Estimation of Laplacian Constrained Precision Matrices Jiaxi Ying, José Miranda Cardoso, Daniel Palomar

NeurIPS 2021 Graphical Models in Heavy-Tailed Markets Jose Vinicius de Miranda Cardoso, Jiaxi Ying, Daniel Palomar

JMLR 2020 A Unified Framework for Structured Graph Learning via Spectral Constraints Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel P. Palomar

NeurIPS 2020 Nonconvex Sparse Graph Learning Under Laplacian Constrained Graphical Model Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel Palomar

NeurIPS 2019 Structured Graph Learning via Laplacian Spectral Constraints Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar