We present a simple and effective approach for posterior uncertainty quantification in deep
operator networks (DeepONets); an emerging paradigm for supervised learning in function …

Abstract Information-directed sampling (IDS) has recently demonstrated its potential as a
data-efficient reinforcement learning algorithm. However, it is still unclear what is the right …

The quintessential model-based reinforcement-learning agent iteratively refines its
estimates or prior beliefs about the true underlying model of the environment. Recent …

Several fundamental problems in science and engineering consist of global optimization
tasks involving unknown high-dimensional (black-box) functions that map a set of …

A fundamental challenge for any intelligent system is prediction: given some inputs, can you
predict corresponding outcomes? Most work on supervised learning has focused on …

Bayesian inference is often used to quantify uncertainty. Several recent analyses have
rigorously decomposed uncertainty in prediction by Bayesian inference into two types: the …

Nowadays, weather prediction is based on numerical models of the physics of the
atmosphere. These models are usually run multiple times based on randomly perturbed …

Reinforcement learning is the paradigm of machine learning dedicated to sequential
decision-making problems. Like many other areas of machine learning and statistics, there …

Uncertainty quantification is a recent emerging interdisciplinary area that leverages the
power of statistical methods, machine learning models, numerical methods and data-driven …

Machine learning studies algorithms for learning from data. Probabilistic modeling and
reasoning define a principled framework for machine learning, where probability theory is …