28 citations · 52 across the 7 of their papers we have counts for
8 papers
Efficient Exploration Is Enough
Mikel Malagón, Jon Vadillo, Josu Ceberio +2
This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we defin…
Fixed Point Explainability
Emanuele La Malfa, Jon Vadillo, Marco Molinari +1
This paper introduces a formal notion of fixed point explanations, inspired by the "why regress" principle, to assess, through recursive applications, the stability of the interpla…
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
Jon Vadillo, Roberto Santana, Jose A. Lozano +1
The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications. Promising approaches…
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Jon Vadillo, Roberto Santana, Jose A. Lozano
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpr…
Analysis of Dominant Classes in Universal Adversarial Perturbations
Jon Vadillo, Roberto Santana, Jose A. Lozano
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to effici…
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
Jon Vadillo, Roberto Santana, Jose A. Lozano
Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models ca…