Area under the ROC curve, aka AUC, is a measure of choice for assessing the performance
of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that …

Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …

Skin cancer affects the lives of millions of people every year, as it is considered the most
popular form of cancer. In the USA alone, approximately three and a half million people are …

Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …

We consider training models with differential privacy (DP) using mini-batch gradients. The
existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD) …

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

Combinatorial features are essential for the success of many commercial models. Manually
crafting these features usually comes with high cost due to the variety, volume and velocity …

Click-through rate prediction is an essential task in industrial applications, such as online
advertising. Recently deep learning based models have been proposed, which follow a …