4 citations · 7 across the 11 of their papers we have counts for
5 papers · 1 filter
Improving the Linearized Laplace Approximation via Quadratic Approximations
Pedro Jiménez, Luis A. Ortega, Pablo Morales-Álvarez +1
Deep neural networks (DNNs) often produce overconfident out-of-distribution predictions, motivating Bayesian uncertainty quantification. The Linearized Laplace Approximation (LLA)…
PAC-Bayes-Chernoff bounds for unbounded losses
Ioar Casado, Luis A. Ortega, Aritz Pérez +1
We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails…
If there is no underfitting, there is no Cold Posterior Effect
Yijie Zhang, Yi-Shan Wu, Luis A. Ortega +1
The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep learning shows that, for posteriors with a temperature , the resulting posterior predictive could have b…
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
The Linearized Laplace Approximation (LLA) has been recently used to perform uncertainty estimation on the predictions of pre-trained deep neural networks (DNNs). However, its wide…
Deep Variational Implicit Processes
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesi…