Search Results for author: Joaquín Míguez

Found 4 papers, 1 papers with code

Training Implicit Generative Models via an Invariant Statistical Loss

1 code implementation26 Feb 2024 José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez, Joaquín Míguez

In this work, we develop a discriminator-free method for training one-dimensional (1D) generative implicit models and subsequently expand this method to accommodate multivariate cases.

Generative Adversarial Network

Convergence rates for optimised adaptive importance samplers

no code implementations28 Mar 2019 Ömer Deniz Akyildiz, Joaquín Míguez

The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $\mathcal{O}(1/\sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided.

A probabilistic incremental proximal gradient method

no code implementations4 Dec 2018 Ömer Deniz Akyildiz, Émilie Chouzenoux, Víctor Elvira, Joaquín Míguez

In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm.

Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization

no code implementations23 Nov 2018 Ömer Deniz Akyildiz, Dan Crisan, Joaquín Míguez

We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that consists of the sum of many individual components.

Stochastic Optimization

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