May 8, 2024 · We give a sparse sketching method running in optimal space and current matrix multiplication time, which recovers a nearly-unbiased least squares estimator ...
May 8, 2024 · Matrix sketching is a powerful tool for reducing the size of large data matrices. Yet there are fundamental limitations to this size reduction ...
Nov 5, 2024 · TL;DR: We give a sparse sketching method running in optimal space and current matrix multiplication time, recovering a nearly-unbiased least ...
In particular, we give a sparse sketching method running in optimal space and current matrix multiplication time, which recovers a nearly-unbiased least squares ...
Co-authors ; Distributed Least Squares in Small Space via Sketching and Bias Reduction. S Garg, K Tan, M Dereziński. arXiv preprint arXiv:2405.05343, 2024. 1 ...
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment ...
Distributed Least Squares in Small Space via Sketching and Bias Reduction ... A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
本论文旨在解决使用矩阵草图技术时，对于任务如最小二乘回归，准确估计器的大小限制问题。 作者通过设计草图方法来最小化估计器的偏差，而不是其误差，从而在分布式设置中 ...
In this area, we wish to estimate statistically the inverse covariance matrix of a population, or some of its functionals, based on a finite number of samples.
Random projections are a popular way of performing ran- domized dimensionality reduction, which are widely used in many computational and learning problems [18] ...
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