Active learning is a powerful tool when labelling data is expensive, but it introduces a bias
because the training data no longer follows the population distribution. We formalize this …

In Bayesian inference, we are usually interested in the numerical approximation of integrals
that are posterior expectations or marginal likelihoods (aka, Bayesian evidence). In this …

Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference.
Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the …

We study adaptive importance sampling (AIS) as an online learning problem and argue for
the importance of the trade‐off between exploration and exploitation in this adaptation …

We show that the standard computational pipeline of probabilistic programming systems
(PPSs) can be inefficient for estimating expectations and introduce the concept of …

Active learning is usually applied to acquire labels of informative data points in supervised
learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy …

Approximate Bayesian inference methods provide a powerful suite of tools for finding
approximations to intractable posterior distributions. However, machine learning …

One fundamental goal of theoretical neuroscience is to understand the normative principles
governing the functional organization of neural circuits, and, in turn, to what extent they can …

Bayesian models capture the intrinsic variability of a data-generating process as a posterior
distribution over the parameters of the model for the process. Decisions that are optimal for a …