Recognizing Variables from Their Data via Deep Embeddings of Distributions A key obstacle in automated analytics and metalearning is failing 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 relying on their arbitrarily selected variable names. Here, we present a computationally efficient method to identify highconfidence 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, and empirically outperform standard statistical techniques for dataset search.

Discovering Reliable Correlations in Categorical Data In many scientific tasks we are interested in finding correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on data distribution or the type of correlation, and, how to search efficiently for the most correlated attribute sets. We answer these questions for discovery tasks with categorical data. In particular, we propose a correctedforchance, consistent, and efficient estimator for normalized total correlation, in order to obtain a reliable, interpretable, and nonparametric measure for correlation over multivariate sets. For the discovery of the topk correlated sets, we derive an effective algorithmic framework based on a tight bounding function. This framework offers exact, approximate, and heuristic search. Empirical evaluation shows that already for small sample sizes the estimator leads to lowregret optimization outcomes, while the algorithms are shown to be highly effective for both large and highdimensional data. Through a case study we confirm that our discovery framework identifies interesting and meaningful correlations.