# Least Squares Auto-Tuning

arXiv: https://arxiv.org/abs/1904.05460 · HTML: https://mlsanity.com/paper/1904.05460/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Total Variation Optimization Layers for Computer Vision](https://mlsanity.com/paper/2204.03643/) (2022)
- [Second-Order Sensitivity Analysis for Bilevel Optimization](https://mlsanity.com/paper/2205.02329/) (2022)
- [Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models](https://mlsanity.com/paper/2305.02573/) (2023)
- [Disciplined Bilevel Programming](https://mlsanity.com/paper/2609.00644/) (2026)
- [Generation of Custom Solvers in Rust for Convex Optimization](https://mlsanity.com/paper/2609.13875/) (2026)

## Abstract (arXiv)

Least squares is by far the simplest and most commonly applied computational method in many fields. In almost all applications, the least squares objective is rarely the true objective. We account for this discrepancy by parametrizing the least squares problem and automatically adjusting these parameters using an optimization algorithm. We apply our method, which we call least squares auto-tuning, to data fitting.