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Corinne Jones

University of Washington



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405 – Student Paper Award and Chambers Statistical Software Award

End-to-End Statistical Learning, with or Without Labels

Sponsor: Section on Statistical Computing
Keywords: discriminative clustering, unsupervised learning, semi-supervised learning, representation learning

Corinne Jones

University of Washington

We introduce an approach that allows one to learn a feature representation and perform clustering of unlabeled data. The approach can also leverage any amount of additional labeled data in order to boost the statistical performance. The proposed method is based on a semi-implicit stochastic optimization algorithm and an entropy-regularized optimal transport algorithm. A numerical illustration on a real dataset shows the promise of the proposed approach.

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