( en | de | ru )

 

Disambiguation

From post

копироватьудалитьдобавить публикацию в буфер
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence.

, , , и . CoRR, (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

Jia Luo

Xiaoguang Luo

Yongxiang Luo

Junfeng Luo

Liangping Luo

 

Другие публикации лиц с тем же именем

Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective., и . CoRR, (2022)Statistical and Computational Limits for Tensor-on-Tensor Association Detection., , , и . COLT, том 195 из Proceedings of Machine Learning Research, стр. 5260-5310. PMLR, (2023)Iterative Approximate Cross-Validation., , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 23083-23102. PMLR, (2023)Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints., и . CoRR, (2024)Diagnosing University Student Subject Proficiency and Predicting Degree Completion in Vector Space., и . AAAI, стр. 7920-7927. AAAI Press, (2018)Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection., и . COLT, том 125 из Proceedings of Machine Learning Research, стр. 3852-3856. PMLR, (2020)Provable Second-Order Riemannian Gauss-Newton Method for Low-Rank Tensor Estimation ‖., , , и . ICASSP, стр. 9057-9061. IEEE, (2022)A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration., , , и . J. Mach. Learn. Res., (2021)Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence., , , и . CoRR, (2020)Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence., и . J. Mach. Learn. Res., (2023)