Many modern machine learning applications come with complex and nuanced design goals
such as minimizing the worst-case error, satisfying a given precision or recall target, or …

The vast majority of statistical theory on binary classification characterizes performance in
terms of accuracy. However, accuracy is known in many cases to poorly reflect the practical …

Early warning prediction systems can suffer from high false alarm rates that limit utility,
especially in settings with high class imbalance such as healthcare. Despite the widespread …

While neural network binary classifiers are often evaluated on metrics such as Accuracy and
$ F_1 $-Score, they are commonly trained with a cross-entropy objective. How can this …

The advent of big data and machine learning (ML) has the potential to enhance the quality
and timeliness of care for hospitalized patients. Through analyzing time series data of lab …

We study statistical properties of the k-nearest neighbors algorithm for multiclass
classification, with a focus on settings where the number of classes may be large and/or …

Accuracy at the top is a special class of binary classification problems where the
performance is evaluated only on a small number of relevant (top) samples. Applications …

Deep learning models have achieved great success in a wide range of areas over the past
decade, like image processing, natural language processing, audio recognition and robot …

We propose a framework which makes it feasible to directly train deep neural networks with
respect to popular families of task-specific non-decomposable performance measures such …

We study the problem of cost-sensitive hierarchical classification where a label taxonomy
has a cost-sensitive loss associated with it, which represents the cost of (wrong) predictions …