14 citations · 28 across the 4 of their papers we have counts for
4 papers
Score-Based Diffusion Models as Principled Priors for Inverse Imaging
Berthy T. Feng, Jamie Smith, Michael Rubinstein +3
Priors are essential for reconstructing images from noisy and/or incomplete measurements. The choice of the prior determines both the quality and uncertainty of recovered images. W…
Ensembling over Classifiers: a Bias-Variance Perspective
Neha Gupta, Jamie Smith, Ben Adlam +1
Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underli…
Understanding the bias-variance tradeoff of Bregman divergences
Ben Adlam, Neha Gupta, Zelda Mariet +1
This paper builds upon the work of Pfau (2013), which generalized the bias variance tradeoff to any Bregman divergence loss function. Pfau (2013) showed that for Bregman divergence…
Estimating the Spectral Density of Large Implicit Matrices
Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson +4
Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of…