Monte Carlo methods represent a cornerstone of computer science. They allow sampling
high dimensional distribution functions in an efficient way. In this paper we consider the …

We propose to apply several gradient estimation techniques to enable the differentiation of
programs with discrete randomness in High Energy Physics. Such programs are common in …

Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the
parameters to be stochastic, were shown to perform competitively with full Bayesian neural …

If the conclusion of a data analysis is sensitive to dropping very few data points, that
conclusion might hinge on the particular data at hand rather than representing a more …

Metropolis-Hastings estimates intractable expectations-can differentiating the algorithm
estimate their gradients? The challenge is that Metropolis-Hastings trajectories are not …

If the conclusion of a data analysis is sensitive to dropping very few data points, that
conclusion might hinge on the particular data at hand rather than representing a more …

Markovian systems are ubiquitous in nature, science, and engineering, to model the
evolution of a system for which the future state of the system only depends on the past …

Esta tesis abarca dos temas principales diferentes: el primero se refiere al estudio de teorías
de gauge de Higgs múltiples en el retículo; mientras que el segundo es un estudio sobre …