Chris Holmes

Diffusion models unlock data-efficient classifier pretraining, outperforming self-supervised methods on challenging medical and time-series datasets with limited data.

FairBED shows that you can design data acquisition processes that inherently reduce bias, leading to fairer machine learning models.

Turns out, focusing on *prognostic* scores might be the key to unlocking more robust causal inference when treatment groups barely overlap.