# Supervising Unsupervised Learning

arXiv: https://arxiv.org/abs/1709.05262 · HTML: https://mlsanity.com/paper/1709.05262/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [How to Train Your MAML to Excel in Few-Shot Classification](https://mlsanity.com/paper/2106.16245/) (2021)
- [Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts](https://mlsanity.com/paper/2204.07312/) (2022)
- [When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications](https://mlsanity.com/paper/2211.08228/) (2022)
- [From Latent to Engine Manifolds: Analyzing ImageBind's Multimodal Embedding Space](https://mlsanity.com/paper/2409.10528/) (2024)
- [Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm](https://mlsanity.com/paper/2401.09376/) (2024)

## Abstract (arXiv)

We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via simple agnostic bounds on unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent the Kleinberg's impossibility result. Experimental results across hundreds of problems demonstrate improved performance on unsupervised data with simple algorithms, despite the fact that our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features from many such small datasets, and perform zero shot learning.