Least Squares Auto-Tuning
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Apr 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
5 papers.
- 2022Total Variation Optimization Layers for Computer Visioncited by 8
- 2022Second-Order Sensitivity Analysis for Bilevel Optimizationcited by 1
- 2023Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Modelscited by 1
- 2026Disciplined Bilevel Programmingcited by 0
- 2026Generation of Custom Solvers in Rust for Convex Optimizationcited by 0
- 2026Generation of Custom Solvers in Rust for Convex Optimization12 Sep 2026
- 2026Disciplined Bilevel Programming1 Sep 2026
- 2023Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models4 May 2023
- 2022Second-Order Sensitivity Analysis for Bilevel Optimization4 May 2022
- 2022Total Variation Optimization Layers for Computer Vision7 Apr 2022