38 citations · 121 across the 18 of their papers we have counts for
19 papers
A unifying Bayesian framework for adversarial robustness
Pablo G. Arce, Roi Naveiro, David Ríos Insua
The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such as adversarial training, typically ro…
Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David Ríos Insua
There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has…
Computational adversarial risk analysis for general security games
Jose Manuel Camacho, Roi Naveiro, David Rios Insua
This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and mult…
Poisoning Bayesian Inference via Data Deletion and Replication
Matthieu Carreau, Roi Naveiro, William N. Caballero
Research in adversarial machine learning (AML) has shown that statistical models are vulnerable to maliciously altered data. However, despite advances in Bayesian machine learning…
Personalized Pricing Decisions Through Adversarial Risk Analysis
Daniel García Rasines, Roi Naveiro, David Ríos Insua +1
Pricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision-making problems, pricing unf…
Manipulating hidden-Markov-model inferences by corrupting batch data
William N. Caballero, Jose Manuel Camacho, Tahir Ekin +1
Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decis…
Simulation Based Bayesian Optimization
Roi Naveiro, Becky Tang
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic sur…
Adversarial attacks against Bayesian forecasting dynamic models
Roi Naveiro
The last decade has seen the rise of Adversarial Machine Learning (AML). This discipline studies how to manipulate data to fool inference engines, and how to protect those systems…
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…
Data sharing games
Víctor Gallego, Roi Naveiro, David Ríos Insua +1
Data sharing issues pervade online social and economic environments. To foster social progress, it is important to develop models of the interaction between data producers and cons…
Adversarial Risk Analysis (Overview)
David Banks, Víctor Gallego, Roi Naveiro +1
Adversarial risk analysis (ARA) is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It enables an analyst to…
Protecting Classifiers From Attacks
Victor Gallego, Roi Naveiro, Alberto Redondo +2
In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willin…
Adversarial Machine Learning: Bayesian Perspectives
David Rios Insua, Roi Naveiro, Victor Gallego +1
Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be advers…
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,…
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…
Gradient Methods for Solving Stackelberg Games
Roi Naveiro, David Ríos Insua
Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical g…
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…
Large Scale Automated Forecasting for Monitoring Network Safety and Security
Roi Naveiro, Simón Rodríguez, David Ríos Insua
Real time large scale streaming data pose major challenges to forecasting, in particular defying the presence of human experts to perform the corresponding analysis. We present her…
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…