Our method can take existing generative models and use them to produce counterfactual images, text, etc.

From a technical perspective, our approach is doubly robust and can be wrapped around state of the art approaches like diffusion models, flow matching, and autoregressive language models.

Same - me since I was 4. CGM is fantastic.

I'm a current Harvard graduate student and I found out today that I had my NSF GRFP terminated without notification. I was awarded this individual research fellowship before even choosing Harvard as my graduate school

Thanks for the pointer! We'll check it out

Our main insight is that smooth divergences - like the Sinkhorn - behave locally like an MMD, and so it suffices to compress with respect to that criterion. This insight draws from recent works studying distributional limits of Sinkhorn divergences (Goldfeld et al., Gonzalez-Sanz et al.).

We build on earlier coreset selection works that compress with respect to maximum mean discrepancy (MMD), including kernel thinning (Dwivedi and @lestermackey.bsky.social) and quadrature (Hayakawa et al.).

We pay special attention to the Sinkhorn divergence from optimal transport. Using our method, CO2, a dataset of size n can be compressed to about size log(n) without meaningful Sinkhorn error.

Agreed. And when misspecified, the MLE is estimating a Kullback-Leibler projection of the true distribution onto the misspecified model (and is consistent for that as n->infinity).