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Poster
Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez · Jeffrey Regier · Michael Jordan · Nir Yosef

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #4
in Tue Poster Session B »
Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework for learning representations that relies on Auto-Encoding Variational Bayes and whose search space is constrained via kernel-based measures of independence. In particular, our method employs the $d$-variable Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between the latent representations and arbitrary nuisance factors. We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations. We also present a full-fledged application to single-cell RNA sequencing (scRNA-seq). In this setting the biological signal in mixed in complex ways with sequencing errors and sampling effects. We show that our method out-performs the state-of-the-art in this domain.
Keywords: Variational Inference · Graphical Models · Unsupervised Learning · Nonlinear Dimensionality Reduction and Manifold Learning · Deep Autoencoders · Generative Models · Computational Biology and Bioinformatics · Latent Variable Models
[ Paper] 

Author Information

Romain Lopez (UC Berkeley)
Jeffrey Regier (UC Berkeley)
Michael Jordan (UC Berkeley)
Nir Yosef (UC Berkeley)

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