52 citations · 65 across the 10 of their papers we have counts for
5 papers · 1 filter
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani +1
Uncertainty estimation is critical in high-stakes machine learning applications. One effective way to estimate uncertainty is conformal prediction, which can provide predictive inf…
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion
Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani +1
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been li…
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion
Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant +3
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformat…
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani +1
Diffusion models achieve state-of-the-art performance in generating realistic objects and have been successfully applied to images, text, and videos. Recent work has shown that dif…
Improving Graph Generation by Restricting Graph Bandwidth
Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang +2
Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However, one of the main limitat…