  A key obstacle in automated analytics and meta-learning is the inability to
recognize when different datasets contain measurements of the same variable.
Because provided attribute labels are often uninformative in practice, this
task may be more robustly addressed by leveraging the data values themselves
rather than just relying on their arbitrarily selected variable names. Here, we
present a computationally efficient method to identify high-confidence variable
matches between a given set of data values and a large repository of previously
encountered datasets. Our approach enjoys numerous advantages over
distributional similarity based techniques because we leverage learned vector
embeddings of datasets which adaptively account for natural forms of data
variation encountered in practice. Based on the neural architecture of deep
sets, our embeddings can be computed for both numeric and string data. In
dataset search and schema matching tasks, our methods outperform standard
statistical techniques and we find that the learned embeddings generalize well
to new data sources.
