Standard conformal prediction methods provide a marginal coverage guarantee, which
means that for a random test point, the conformal prediction set contains the true label with a …

Predictive modeling for toxicity can help reduce risks in a range of applications and
potentially serve as the basis for regulatory decisions. However, the utility of these …

The field of distribution-free predictive inference provides tools for provably valid prediction
without any assumptions on the distribution of the data, which can be paired with any …

Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain
datasets that contribute towards international accords. However, these datasets contain …

Conformal prediction is a statistical framework that generates prediction sets containing
ground-truth labels with a desired coverage guarantee. The predicted probabilities …

Making predictions with an associated confidence is highly desirable as it facilitates decision
making and resource prioritization. Conformal regression is a machine learning framework …

High-throughput screening, where thousands of molecules rapidly can be assessed for
activity against a protein, has been the dominating approach in drug discovery for many …

The assessment of compound cytotoxicity is an important part of the drug discovery process.
Accurate predictions of cytotoxicity have the potential to expedite decision making and save …

As the semiconductor industry has shifted to a fabless paradigm, the risk of hardware
Trojans being inserted at various stages of production has also increased. Recently, there …

Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework,
reliably provides valid predictive inference for black-box models. CP constructs prediction …