6 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…
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…
Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization
Andre Carlon, Luis Espath, Raul Tempone
Using quasi-Newton methods in stochastic optimization is not a trivial task given the difficulty of extracting curvature information from the noisy gradients. Moreover, pre-conditi…
Multi-Iteration Stochastic Optimizers
Andre Carlon, Luis Espath, Rafael Lopez +1
We introduce Multi-Iteration Stochastic Optimizers, a novel class of first-order stochastic methods that control the relative error using successive control variates along th…
Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization
Andre Gustavo Carlon, Ben Mansour Dia, Luis FR Espath +2
Finding the best setup for experiments is the primary concern for Optimal Experimental Design (OED). Here, we focus on the Bayesian experimental design problem of finding the setup…