79 citations · 91 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods
Luis Espath, Sebastian Krumscheid, Raúl Tempone +1
In this study, we demonstrate that the norm test and inner product/orthogonality test presented in \cite{Bol18} are equivalent in terms of the convergence rates associated with Sto…
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…