Supervising unsupervised learning

Vikas K. Garg, Adam Kalai

Tutkimustuotos: LehtiartikkeliConference articleScientificvertaisarvioitu

12 Sitaatiot (Scopus)


We introduce a framework to transfer 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 rigorous agnostic bounds on a variety of 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 Kleinberg's impossibility result. Experiments across hundreds of problems demonstrate improvements in performance on unsupervised data with simple algorithms despite the fact our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features across many small datasets, and perform zero shot learning.

JulkaisuAdvances in Neural Information Processing Systems
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on Neural Information Processing Systems - Palais des Congrès de Montréal, Montréal, Kanada
Kesto: 2 jouluk. 20188 jouluk. 2018
Konferenssinumero: 32


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