Robotic applications often involve working in environments that are uncertain, dynamic, and
partially observable. Recently, diffusion models have been proposed for learning trajectory …

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to
determine the confidence values of new predictions. We propose an inductive conformal …

The deployment of safe and trustworthy machine learning systems and particularly complex
black box neural networks in real-world applications requires reliable and certified …

Machine learning components such as deep neural networks are used extensively in cyber-
physical systems (CPS). However, such components may introduce new types of hazards …

Given a family of pretrained models and a hold-out set, how can we construct a valid
conformal prediction set while selecting a model that minimizes the width of the set? If we …

Machine learning components such as deep neural networks are used extensively in Cyber-
Physical Systems (CPS). However, they may introduce new types of hazards that can have …

Autonomous Cyber-Physical Systems (CPS) must be robust against potential failure modes,
including physical degradations and software issues, and are required to self-manage …

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to
determine the confidence values of new predictions. We propose a conformal prediction …

With the proliferation of increasingly complicated Deep Learning architectures, data
synthesis is a highly promising technique to address the demand of data-hungry models …

Deep Learning predictions with measurable confidence are increasingly desirable for real-
world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is …