Data is a central component of machine learning and causal inference tasks. The availability
of large amounts of data from sources such as open data repositories, data lakes and data …

Language models and specialized table embedding models have recently demonstrated
strong performance on many tasks over tabular data. Researchers and practitioners are …

Machine-learning from a disparate set of tables, a data lake, requires assembling features
by merging and aggregating tables. Data discovery can extend autoML to data tables by …

Data discovery is a major challenge in enterprise data analysis: users often struggle to find
data relevant to their analysis goals or even to navigate through data across data sources …

Designing effective data manipulation methods is a long standing problem in data lakes.
Traditional methods, which rely on rules or machine learning models, require extensive …

Using data from different data sources is a common procedure in data-driven research. As
required data is often not available from centrally managed sources, the concept of data …

Assessment is the process of comparing the actual to the expected behavior of a business
phenomenon and judging the outcome of the comparison. The querying operator has been …

Data lakes are massive repositories of raw and heterogeneous data, designed to meet the
requirements of modern data storage. Nonetheless, this same philosophy increases the …

Dataset search is a long-standing problem across both industry and academia. While most
industry tools focus on identifying one or more datasets matching a user-specified query …

The increasing amount of data being collected, stored, and analyzed, induces a need for
efficient, scalable, and robust methods to handle this data. Representation learning, ie, the …