7 papers
Shrinkage through multiple identifiability
Carlos GarcÃa Meixide, David RÃos Insua
We propose an empirical Bayes framework for combining estimators obtained from multiple identification functionals associated with the same estimand. We adaptively pool a collectio…
A probabilistic framework for online test-time adaptation
Daniel Corrales, David RÃos Insua
This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but must adapt to unlabeled data at test time un…
A unifying Bayesian framework for adversarial robustness
Pablo G. Arce, Roi Naveiro, David RÃos Insua
The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such as adversarial training, typically ro…
Predictive posteriors under hidden confounding
Carlos GarcÃa Meixide, David RÃos Insua
Predicting outcomes in external domains is challenging due to hidden confounders that potentially influence both predictors and outcomes. Well-established methods frequently rely o…
Evasion Attacks Against Bayesian Predictive Models
Pablo G. Arce, Roi Naveiro, David RÃos 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…
Supporting product launching decisions with adversarial risk analysis
Pablo G. Arce, Sonali Das, David RÃos Insua
In a world of utility-driven marketing, each company acts as an adversary to other contenders, with all having competing interests. A major challenge for companies launching a new…