3 papers
stat.ML2026
Covariance-aware sampling for Diffusion Models
Andrea Schioppa, Tim Salimans
We present a covariance-aware sampler that improves the quality of pixel-space Diffusion Model (DM) sampling in the few-step regime. We hypothesize that in the few-step regime samp…
cs.LG2024
Model Integrity when Unlearning with T2I Diffusion Models
Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek
The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes ge…
cs.LG2024
Efficient Sketches for Training Data Attribution and Studying the Loss Landscape
Andrea Schioppa
The study of modern machine learning models often necessitates storing vast quantities of gradients or Hessian vector products (HVPs). Traditional sketching methods struggle to sca…