In this survey, we design formal verification and control algorithms for autonomous systems
with practical safety guarantees using conformal prediction (CP), a statistical tool for …

The safe integration of machine learning modules in decision-making processes hinges on
their ability to quantify uncertainty. A popular technique to achieve this goal is conformal …

In modern wireless network architectures, such as Open Radio Access Network (O-RAN),
the operation of the radio access network (RAN) is managed by applications, or apps for …

This paper proposes the use of" multicalibration" to yield interpretable and reliable
confidence scores for outputs generated by large language models (LLMs). Multicalibration …

Conformal prediction is a statistical tool for producing prediction regions for machine
learning models that are valid with high probability. A key component of conformal prediction …

We introduce adaptive learn-then-test (aLTT), an efficient hyperparameter selection
procedure that provides finite-sample statistical guarantees on the population risk of AI …

The application of artificial intelligence (AI) models in fields such as engineering is limited by
the known difficulty of quantifying the reliability of an AI's decision. A well-calibrated AI model …

Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that
offers worst-case deterministic long-term risk control, as well as statistical marginal coverage …

Current research on trajectory prediction primarily relies on data collected by onboard
sensors of an ego vehicle. With the rapid advancement in connected technologies, such as …

Theexisting work on the distributed training of machine learning (ML) models has
consistently overlooked the distribution of the achieved learning quality, focusing instead on …