79 citations · 91 across the 12 of their papers we have counts for
15 papers
Efficient Stochastic BFGS methods Inspired by Bayesian Principles
André Carlon, Luis Espath, Raúl Tempone
Quasi-Newton methods are ubiquitous in deterministic local search due to their efficiency and low computational cost. This class of methods uses the history of gradient evaluations…
Multilevel randomized quasi-Monte Carlo estimator for nested integration
Arved Bartuska, André Gustavo Carlon, Luis Espath +2
Nested integration problems arise in various scientific and engineering applications, including Bayesian experimental design, financial risk assessment, and uncertainty quantificat…
Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty
Arved Bartuska, Luis Espath, Raúl Tempone
Finding the optimal design of experiments in the Bayesian setting typically requires estimation and optimization of the expected information gain functional. This functional consis…
Scalable method for Bayesian experimental design without integrating over posterior distribution
Vinh Hoang, Luis Espath, Sebastian Krumscheid +1
We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational map is based on partial differential equations…
Physics-informed Spectral Learning: the Discrete Helmholtz--Hodge Decomposition
Luis Espath, Pouria Behnoudfar, Raul Tempone
In this work, we further develop the Physics-informed Spectral Learning (PiSL) by Espath et al. \cite{Esp21} based on a discrete projection to solve the discrete Hodge--Helmh…
Double-loop randomized quasi-Monte Carlo estimator for nested integration
Arved Bartuska, André Gustavo Carlon, Luis Espath +2
Nested integration of the form $\int f\left(\int g(\bs{y},\bs{x})\di{}\bs{x}\right)\di{}\bs{y}$, characterized by an outer integral connected to an inner integral through a nonline…