38 citations · 112 across the 12 of their papers we have counts for
13 papers
Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning
M. Santos-Pascual, D. Ríos Insua
Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertainty. This work analyses adversar…
A Bayesian network model for predicting cardiovascular risk
J. M. Ordovas, D. Rios Insua, A. Santos-Lozano +4
We propose a Bayesian network model to make inferences and predictions about cardiovascular risk. Both the structure and the probability tables in the underlying model are built us…
Managing driving modes in automated driving systems
David Ríos Insua, William N. Caballero, Roi Naveiro
Current technologies are unable to produce massively deployable, fully autonomous vehicles that do not require human intervention. Such technological limitations are projected to p…
Insider threat modeling: An adversarial risk analysis approach
Chaitanya Joshi, David Rios Insua, Jesus Rios
Insider threats entail major security issues in geopolitics, cyber risk management and business organization. The game theoretic models proposed so far do not take into account som…
Protecting from Malware Obfuscation Attacks through Adversarial Risk Analysis
Alberto Redondo, David Rios Insua
Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed thro…
Augmented Probability Simulation Methods for Sequential Games
Tahir Ekin, Roi Naveiro, Alberto Torres-Barrán +1
We present a robust framework with computational algorithms to support decision makers in sequential games. Our framework includes methods to solve games with complete information,…
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs
Victor Gallego, David Rios Insua
A framework to boost the efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation. We call it th…
Opponent Aware Reinforcement Learning
Victor Gallego, Roi Naveiro, David Rios Insua +1
In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit. We introduce Threatened Markov…
An Adversarial Risk Analysis Framework for Cybersecurity
David Rios Insua, Aitor Couce Vieira, Jose Antonio Rubio +3
Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially a…
Stochastic Gradient MCMC with Repulsive Forces
Victor Gallego, David Rios Insua
We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), lea…
Reinforcement Learning under Threats
Victor Gallego, Roi Naveiro, David Rios Insua
In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. In this paper, we in…
Adversarial classification: An adversarial risk analysis approach
Roi Naveiro, Alberto Redondo, David Ríos Insua +1
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approach…
A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity
Aitor Couce Vieira, Siv Hilde Houmb, David Rios Insua
Oil and gas drilling is based, increasingly, on operational technology, whose cybersecurity is complicated by several challenges. We propose a graphical model for cybersecurity ris…