Lecture Notes: Optimization for Machine Learning
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Sep 2019.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 2; 2026: 2.
8 papers.
- 2022Symmetry Teleportation for Accelerated Optimizationcited by 25
- 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directionscited by 17
- 2022Fast Nonlinear Vector Quantile Regressioncited by 12
- 2022Adaptive Gradient Methods with Local Guaranteescited by 8
- 2026StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Modelscited by 5
- 2025Teleportation With Null Space Gradient Projection for Optimization Accelerationcited by 2
- 2025Regret Analysis: a control perspectivecited by 0
- 2026Quadratic Upper Bound for Boosting Robustnesscited by 0
- 2026StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models16 Apr 2026
- 2026Quadratic Upper Bound for Boosting Robustness20 Jan 2026
- 2025Teleportation With Null Space Gradient Projection for Optimization Acceleration17 Feb 2025
- 2025Regret Analysis: a control perspective8 Jan 2025
- 2023Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions7 Feb 2023
- 2022Fast Nonlinear Vector Quantile Regression30 May 2022
- 2022Symmetry Teleportation for Accelerated Optimization21 May 2022
- 2022Adaptive Gradient Methods with Local Guarantees2 Mar 2022