# Lectures on Randomized Numerical Linear Algebra

arXiv: https://arxiv.org/abs/1712.08880 · HTML: https://mlsanity.com/paper/1712.08880/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2017.

## Built on by (most cited first, 19 of 19)

- [Full Stack Optimization of Transformer Inference: a Survey](https://mlsanity.com/paper/2302.14017/) (2023)
- [Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update](https://mlsanity.com/paper/2107.07480/) (2021)
- [Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning](https://mlsanity.com/paper/2406.11151/) (2024)
- [Sketched Ridgeless Linear Regression: The Role of Downsampling](https://mlsanity.com/paper/2302.01088/) (2023)
- [Sketch-and-Lift: Scalable Subsampled Semidefinite Program for K-means Clustering](https://mlsanity.com/paper/2201.08226/) (2022)
- [Feature Space Sketching for Logistic Regression](https://mlsanity.com/paper/2303.14284/) (2023)
- [PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training](https://mlsanity.com/paper/2601.22137/) (2026)
- [Efficient Backpropagation with Variance-Controlled Adaptive Sampling](https://mlsanity.com/paper/2402.17227/) (2024)
- [Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values](https://mlsanity.com/paper/2410.08336/) (2024)
- [Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton](https://mlsanity.com/paper/2502.13583/) (2025)
- [Quantifying Epistemic Uncertainty in Diffusion Models](https://mlsanity.com/paper/2602.09170/) (2026)
- [Improved large-scale graph learning through ridge spectral sparsification](https://mlsanity.com/paper/2604.20078/) (2026)
- [Toeplitz Least Squares Problems, Fast Algorithms and Big Data](https://mlsanity.com/paper/2112.12994/) (2021)
- [SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data](https://mlsanity.com/paper/2401.00122/) (2023)
- [A Provably Accurate Randomized Sampling Algorithm for Logistic Regression](https://mlsanity.com/paper/2402.16326/) (2024)
- [On confidence intervals for precision matrices and the eigendecomposition of covariance matrices](https://mlsanity.com/paper/2208.11977/) (2022)
- [On the Noise Sensitivity of the Randomized SVD](https://mlsanity.com/paper/2305.17435/) (2023)
- [Importance Sampling for Nonlinear Models](https://mlsanity.com/paper/2505.12353/) (2025)
- [The Advantages of Fresh Sketching for Ridge Regression](https://mlsanity.com/paper/2609.38565/) (2026)

## Abstract (arXiv)

This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data.