Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

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 …

Learning sophisticated feature interactions behind user behaviors is critical in maximizing
CTR for recommender systems. Despite great progress, existing methods seem to have a …

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 …

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 …

We propose a parameter server framework for distributed machine learning problems. Both
data and workloads are distributed over worker nodes, while the server nodes maintain …

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 …

Abstract Machine learning offers a fantastically powerful toolkit for building useful
complexprediction systems quickly. This paper argues it is dangerous to think ofthese quick …

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 …

Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …