David L. Donoho — accepted AI-conference papers

2025

Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World

Joshua Kazdan, Rylan Schaeffer, Apratim Dey, Matthias Gerstgrasser, Rafael Rafailov, David L. Donoho +1

ICML 2025poster

What happens when generative machine learning models are pretrained on web-scale datasets containing data generated by earlier models? Some prior work warns of “model collapse” as the web is overwhelmed by synthetic data; other work suggests the problem can be contained (i.e. collapse can be avoided…

Cited by 8 Source ↗PDF ↗Scholar ↗

2025

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track

Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch, Brando Miranda, Matthias Gerstgrasser, Susan Zhang +8

NeurIPS 2025oral

Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of publications, but have also led to misleading, incorrect, flawed or perhaps even fraudulent studies being accepted and s…

Cited by 0 Source ↗Scholar ↗

2022

Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path

X.Y. Han, Vardan Papyan, David L. Donoho

ICLR 2022oral

The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net training paradigm of driving cross-entropy (CE) loss towards zero. During NC, last-layer features collapse to their class-means, both classifiers and class-means collapse to the same Simplex Equiangular Ti…

Cited by 171 Source ↗PDF ↗code ↗Scholar ↗