Online advertising has historically been approached as user targeting and ad-to-user matching problems within sophisticated optimization algorithms. As the research area and ad tech industry have progressed over the last couple of decades, advertisers ...

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and ...

This tutorial presents state-of-the-art research on causal inference from network data in the presence of interference. We start by motivating research in this area with real-world applications, such as measuring influence in social networks and market ...

One can hear "Data Science" defined as a synonym for machine learning or as a branch of Statistics. I shall argue that it is far more than that; it is the natural evolution of the technology of very large-scale data management to solve problems in ...

Time series data are ubiquitous. Rapid advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. This has resulted in a ...

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real ...

Machine learning has allowed many systems that we interact with to improve performance and personalize. Recommender systems in particular are one of the largest users of machine learning in production environments that have improved performance of real-...

It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be used to evaluate other treatment assignment policies to guide future innovation or experiments. However, ...

Attributed event sequences are commonly encountered in practice. A recent research line focuses on incorporating neural networks with the statistical model--marked point processes, which is the conventional tool for dealing with attributed event ...

Important tasks like record linkage and extreme classification demonstrate extreme class imbalance, with 1 minority instance to every 1 million or more majority instances. Obtaining a sufficient sample of all classes, even just to achieve statistically-...

In this paper, we consider the task of ranking individuals based on the potential benefit of being "treated" (e.g. by a drug or exposure to recommendations or ads), referred to as Uplift Modeling in the literature. This application has gained a surge of ...

Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set). While there are many models for individual ...

The increased availability of multivariate time-series asks for the development of suitable methods able to holistically analyse them. To this aim, we propose a novel flexible method for data-mining, forecasting and causal patterns detection that ...

We present Bavarian, a collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) of all vertices in a graph. Our algorithms use Monte-Carlo Empirical Rademacher Averages (MCERAs), a concept from statistical learning ...

Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (...

Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate ...

How can we efficiently correlate multiple stocks for accurate stock movement prediction? Stock movement prediction has received growing interest in data mining and machine learning communities due to its substantial impact on financial markets. One way ...

Given a time-series vector, how can we efficiently detect anomalies? A widely used method is to use Fast Fourier transform (FFT) to compute Fourier coefficients, take first few coefficients while discarding the remaining small coefficients, and ...

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing ...

Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the development of quantitative metrics to measure algorithm disparities across ...