# Thresholds of descending algorithms in inference problems

arXiv: https://arxiv.org/abs/2001.00479 · HTML: https://mlsanity.com/paper/2001.00479/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Sudakov-Fernique post-AMP, and a new proof of the local convexity of the TAP free energy](https://mlsanity.com/paper/2208.09550/) (2022)
- [Optimal learning rate schedules in high-dimensional non-convex optimization problems](https://mlsanity.com/paper/2202.04509/) (2022)
- [High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models](https://mlsanity.com/paper/2204.04476/) (2022)

## Abstract (arXiv)

We review recent works on analyzing the dynamics of gradient-based algorithms in a prototypical statistical inference problem. Using methods and insights from the physics of glassy systems, these works showed how to understand quantitatively and qualitatively the performance of gradient-based algorithms. Here we review the key results and their interpretation in non-technical terms accessible to a wide audience of physicists in the context of related works.