Turing lecture: The mathematics of large machine learning models
APA
(2025). Turing lecture: The mathematics of large machine learning models. SciVideos. https://scivideos.org/index.php/icts-tifr/32487
MLA
Turing lecture: The mathematics of large machine learning models. SciVideos, Aug. 10, 2025, https://scivideos.org/index.php/icts-tifr/32487
BibTex
@misc{ scivideos_ICTS:32487,
doi = {},
url = {https://scivideos.org/index.php/icts-tifr/32487},
author = {},
keywords = {},
language = {en},
title = {Turing lecture: The mathematics of large machine learning models},
publisher = {},
year = {2025},
month = {aug},
note = {ICTS:32487 see, \url{https://scivideos.org/index.php/icts-tifr/32487}}
}
Abstract
The success of modern AI models defies classical theoretical wisdom. Classical theory recommended the use of convex optimization, and yet AI models learn by optimizing highly non-convex function. Classical theory prescribed to control model complexity and yet AI models are very complex, so complex that they often memorize the training data. Classical wisdom recommends a careful and interpretable choice of model architecture, and yet modern architectures rarely offer a parsimonious representation of a target distribution class.
The discovery that learning can take place in completely unexpected scenario poses beautiful conceptual challenges. I will try to survey recent work towards addressing them.