Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Data distributions in streaming environments are usually not stationary. In order to maintain
a high predictive quality at all times, online learning models need to adapt to distributional …

High-performing predictive models, such as neural nets, usually operate as black boxes,
which raises serious concerns about their interpretability. Local feature attribution methods …

As complex machine learning models are increasingly used in sensitive applications like
banking, trading or credit scoring, there is a growing demand for reliable explanation …

Data streams are ubiquitous in modern business and society. In practice, data streams may
evolve over time and cannot be stored indefinitely. Effective and transparent machine …

Due to the unspecified and dynamic nature of data streams, online machine learning
requires powerful and flexible solutions. However, evaluating online machine learning …

Screening feature selection methods are often used as a preprocessing step for reducing
the number of variables before training a model. Traditional screening methods only focus …

Rapid technological advances are inherently linked to the increased amount of data, a
substantial portion of which can be interpreted as data stream, capable of exhibiting the …

Data streams are ubiquitous in many areas of modern life. For example, applications in
healthcare, education, finance, or advertising often deal with large-scale and evolving data …

Real-world production systems often grapple with maintaining data quality in large-scale,
dynamic streams. We introduce Drifter, an efficient and lightweight system for online feature …