Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Simple Summary One of the hottest areas in deep learning is computerized tumor diagnosis
and treatment. The identification of tumor markers, the outline of tumor growth activity, and …

Data augmentation is a widely used trick when training deep neural networks: in addition to
the original data, properly transformed data are also added to the training set. However, to …

Tree boosting has empirically proven to be a highly effective approach to predictive
modeling. It has shown remarkable results for a vast array of problems. For many years …

Automated AI classifiers should be able to defer the prediction to a human decision maker to
ensure more accurate predictions. In this work, we jointly train a classifier with a rejector …

Scoring systems are linear classification models that only require users to add, subtract and
multiply a few small numbers in order to make a prediction. These models are in widespread …

Sorting is used pervasively in machine learning, either to define elementary algorithms, such
as $ k $-nearest neighbors ($ k $-NN) rules, or to define test-time metrics, such as top-$ k …

Most high-dimensional estimation methods propose to minimize a cost function (empirical
risk) that is a sum of losses associated to each data point (each example). In this paper, we …

Prediction problems often admit competing models that perform almost equally well. This
effect challenges key assumptions in machine learning when competing models assign …

Importance Identifying patients at high risk of adverse outcomes prior to surgery may allow
for interventions associated with improved postoperative outcomes; however, few tools exist …