Machine learning is increasingly applied in high-stakes decision making that directly affect
people's lives, and this leads to an increased demand for systems to explain their decisions …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the
precise execution of the corresponding downstream processes (inbound and outbound …

Machine learning (ML) training data is often scattered across disparate collections of
datasets, called data silos. This fragmentation poses a major challenge for data-intensive …

In machine learning, continuously retraining a model guarantees accurate predictions based
on the latest data as training input. But to retrieve the latest data from a database, time …

Modern data analytics workloads combine relational data processing with machine learning
(ML). Most DBMS handle these workloads by offloading these ML operations to external …

Tensor programs often need to process large tensors (vectors, matrices, or higher order
tensors) that require a specialized storage format for their memory layout. Several such …

In today's data-driven world, it is critical that we use appropriate datasets for analysis and
decision-making. Datasets could be biased because they reflect existing inequalities in the …

This paper introduces semi-ring dictionaries, a powerful class of compositional and purely
functional collections that subsume other collection types such as sets, multisets, arrays …

We introduce indexed streams, a formal operational model and intermediate representation
that describes the fused execution of a contraction language that encompasses both sparse …