Learning probabilistic predictive models that are well calibrated is critical for many prediction
and decision-making tasks in artificial intelligence. In this paper we present a new non …

This paper provides new insight into maximizing F1 measures in the context of binary
classification and also in the context of multilabel classification. The harmonic mean of …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

When evaluating the efficacy of social programs and medical treatments using randomized
experiments, the estimated overall average causal effect alone is often of limited value and …

Noisy PN learning is the problem of binary classification when training examples may be
mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for …

Modern health data science applications leverage abundant molecular and electronic health
data, providing opportunities for machine learning to build statistical models to support …

An accurate model of patient-specific kidney graft survival distributions can help to improve
shared-decision making in the treatment and care of patients. In this paper, we propose a …

Confidence calibration, which aims to make model predictions equal to the true correctness
measures, is important for neural machine translation (NMT) because it is able to offer useful …

Risk scores are simple classification models that let users make quick risk predictions by
adding and subtracting a few small numbers. These models are widely used in medicine …

In user-facing applications, displaying calibrated confidence measures---probabilities that
correspond to true frequency---can be as important as obtaining high accuracy. We are …