38 citations · 121 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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 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…