# Recognizing Variables from their Data via Deep Embeddings of Distributions

arXiv: https://arxiv.org/abs/1909.04844 · HTML: https://mlsanity.com/paper/1909.04844/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Personalized Federated Learning via Heterogeneous Modular Networks](https://mlsanity.com/paper/2210.14830/) (2022)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science](https://mlsanity.com/paper/2303.02204/) (2023)
- [Deep Learning to Jointly Schema Match, Impute, and Transform Databases](https://mlsanity.com/paper/2207.03536/) (2022)

## Abstract (arXiv)

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.