We investigate the statistical and computational limits of latent\textbf {Di} ffusion\textbf {T}
ransformers (\textbf {DiT} s) under the low-dimensional linear latent space assumption …

We introduce a refined differentially private (DP) data structure for kernel density estimation
(KDE), offering not only improved privacy-utility tradeoff but also better efficiency over prior …

De novo protein design (DNPD) aims to create new protein sequences from scratch, without
relying on existing protein templates. However, current deep learning-based DNPD …

Generative modeling of discrete data underlies important applications spanning text-based
agents like ChatGPT to the design of the very building blocks of life in protein sequences …

Deep generative models that learn from the distribution of natural protein sequences and
structures may enable the design of new proteins with valuable functions. While the majority …

Recent years have witnessed a surge in the development of protein foundation models,
significantly improving performance in protein prediction and generative tasks ranging from …

Generative models on discrete state-spaces have a wide range of potential applications,
particularly in the domain of natural sciences. In continuous state-spaces, controllable and …

Cryo-electron microscopy (cryo-EM) is a powerful technique in structural biology and drug
discovery, enabling the study of biomolecules at high resolution. Significant advancements …

Humanization is a critical process for designing efficiently specific antibodies and
nanobodies prior to clinical trials. Developing widely recognized deep learning techniques …