# Lecture Notes: Optimization for Machine Learning

arXiv: https://arxiv.org/abs/1909.03550 · HTML: https://mlsanity.com/paper/1909.03550/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [Symmetry Teleportation for Accelerated Optimization](https://mlsanity.com/paper/2205.10637/) (2022)
- [Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions](https://mlsanity.com/paper/2302.03764/) (2023)
- [Fast Nonlinear Vector Quantile Regression](https://mlsanity.com/paper/2205.14977/) (2022)
- [Adaptive Gradient Methods with Local Guarantees](https://mlsanity.com/paper/2203.01400/) (2022)
- [StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models](https://mlsanity.com/paper/2604.15416/) (2026)
- [Teleportation With Null Space Gradient Projection for Optimization Acceleration](https://mlsanity.com/paper/2502.11362/) (2025)
- [Regret Analysis: a control perspective](https://mlsanity.com/paper/2501.04572/) (2025)
- [Quadratic Upper Bound for Boosting Robustness](https://mlsanity.com/paper/2601.13645/) (2026)

## Abstract (arXiv)

Lecture notes on optimization for machine learning, derived from a course at Princeton University and tutorials given in MLSS, Buenos Aires, as well as Simons Foundation, Berkeley.