Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow
the explicit specification of prior knowledge and accurately capture model uncertainty. We …

Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to
Gaussian Processes (GPs), NPs define distributions over functions and can estimate …

Neural network approaches for meta-learning distributions over functions have desirable
properties such as increased flexibility and a reduced complexity of inference. Building on …

The learning to defer (L2D) framework allows autonomous systems to be safe and robust by
allocating difficult decisions to a human expert. All existing work on L2D assumes that each …

The standard approaches to neural network implementation yield powerful function
approximation capabilities but are limited in their abilities to learn meta representations and …

Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning
models which produce well-calibrated predictions and are trainable via a simple maximum …

We study the task of spatio-temporal extrapolation that generates data at target locations
from surrounding contexts in a graph. This task is crucial as sensors that collect data are …

Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous
methods employ the empirical risk minimization principle in optimization. However, the …