4 papers
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Jaeyeon Kim, Kulin Shah, Vasilis Kontonis +2
In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (A…
ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems
Aayush Karan, Kulin Shah, Sitan Chen
There has been a flurry of activity around using pretrained diffusion models as informed data priors for solving inverse problems, and more generally around steering these models u…
Learning general Gaussian mixtures with efficient score matching
Sitan Chen, Vasilis Kontonis, Kulin Shah
We study the problem of learning mixtures of Gaussians in dimensions. We make no separation assumptions on the underlying mixture components: we only require that the covar…
Unrolled denoising networks provably learn optimal Bayesian inference
Aayush Karan, Kulin Shah, Sitan Chen +1
Much of Bayesian inference centers around the design of estimators for inverse problems which are optimal assuming the data comes from a known prior. But what do these optimality g…