Titlebook: Epistemic Uncertainty in Artificial Intelligence ; First International Fabio Cuzzolin,Maryam Sultana Conference proceedings 2024 The Edito

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,Towards Offline Reinforcement Learning with Pessimistic Value Priors,heuristic policy constraints, value regularisation or uncertainty penalties to achieve successful offline RL policies in a toy environment. An additional consequence of our work is a principled quantification of Bayesian uncertainty in off-policy returns in model-free RL. While we are able to presen

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,A Novel Bayes’ Theorem for Upper Probabilities,lies in a class of probability measures . and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper b

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,Defensive Perception: Estimation and Monitoring of Neural Network Performance Under Deployment,entation in autonomous driving. Our approach is based on the idea that deep learning-based perception for autonomous driving is uncertain and best represented as a probability distribution. As autonomous vehicles’ safety is paramount, it is crucial for perception systems to recognize when the vehicl

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,Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet,, have put the societal impacts of these technologies at the center of public debate. These tools are possible due to the massive amount of data (text and images) that is publicly available through the Internet. At the same time, these generative AI tools become content creators that are already con

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,Towards Offline Reinforcement Learning with Pessimistic Value Priors,y interacting with the environment. As the agent tries to improve on the policy present in the dataset, it can introduce distributional shift between the training data and the suggested agent’s policy which can lead to poor performance. To avoid the agent assigning high values to out-of-distribution

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,Semantic Attribution for Explainable Uncertainty Quantification,reting and explaining the origins and reasons for uncertainty presents a significant challenge. In this paper, we present semantic uncertainty attribution as a tool for pinpointing the primary factors contributing to uncertainty. This approach allows us to explain why a particular image carries high