Uploaded August 2026 | Updated September 2026, 2 weeks ago
Ahmed El Alaoui (Cornell)
https://simons.berkeley.edu/talks/ahmed-el-alaoui-cornell-2026-08-06
Diffusion Generative Modeling: Progress and Next Steps
We consider classifying labelled data in the interpolation regime where there exist linear classifiers (with possibly negative margin) correctly classifying all points in the dataset. Under two idealized data-generating distributions, we establish a large deviation principle on the event that a point chosen uniformly at random from the set of interpolators achieves a given generalization error. This allows us to describe the proportion of interpolators having any desired performance and establish a concentration result regarding the most typical performance. We remark that this typical performance is inferior to that of ERM by gradient descent, indicating that most interpolators are ‘bad’. Sampling such typical interpolators is an interesting open problem.
This is based on joint work with August Chen.
Ahmed El Alaoui (Cornell)
https://simons.berkeley.edu/talks/ahmed-el-alaoui-cornell-2026-08-06
Diffusion Generative Modeling: Progress and Next Steps
We consider classifying labelled data in the interpolation regime where there exist linear classifiers (with possibly negative margin) correctly classifying all points in the dataset. Under two idealized data-generating distributions, we establish a large deviation principle on the event that a point chosen uniformly at random from the set of interpolators achieves a given generalization error. This allows us to describe the proportion of interpolators having any desired performance and establish a concentration result regarding the most typical performance. We remark that this typical performance is inferior to that of ERM by gradient descent, indicating that most interpolators are ‘bad’. Sampling such typical interpolators is an interesting open problem.
This is based on joint work with August Chen.
