Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate
the risk of uncertainty region misspecification in safety-critical settings. Current approaches …

This paper introduces Wasserstein variational inference, a new form of approximate
Bayesian inference based on optimal transport theory. Wasserstein variational inference …

Stochastic infectious disease models capture uncertainty in public health outcomes and
have become increasingly popular in epidemiological practice. However, calibrating these …

In images collected by astronomical surveys, stars and galaxies often overlap visually.
Deblending is the task of distinguishing and characterizing individual light sources in survey …

Although attention-based multi-instance learning algorithms have achieved impressive
performances on slide-level whole slide image (WSI) classification tasks, they are prone to …

Differentiable causal discovery has made significant advancements in the learning of
directed acyclic graphs. However, its application to real-world datasets remains restricted …

For training an encoder network to perform amortized variational inference, the Kullback-
Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusive …

We present a new probabilistic method for detecting, deblending, and cataloging
astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based …

Differentiable causal discovery has made significant advancements in the learning of
directed acyclic graphs. However, its application to real-world datasets remains restricted …

Population receptive field (pRF) mapping is an important asset for cognitive neuroscience.
The pRF model is used for estimating retinotopy, defining functional localizers and to study a …