Fast and Sample-Efficient Federated Low Rank Matrix Recovery From Column-Wise Linear and Quadratic Projections

Author:

Nayer Seyedehsara1ORCID,Vaswani Namrata2ORCID

Affiliation:

1. ASML, Santa Clara, CA, USA

2. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Federated Low Rank Matrix Recovery via Alternating GD and Minimization: A Simple Proof;IEEE Transactions on Information Theory;2024-07

2. Fast and Sample-Efficient Relevance-Based Multi-Task Representation Learning;IEEE Control Systems Letters;2024

3. A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing;2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton);2023-09-26

4. Fast Federated Low Rank Matrix Completion;2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton);2023-09-26

5. Approximately low-rank recovery from noisy and local measurements by convex program;Information and Inference: A Journal of the IMA;2023-04-27

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