1.7k citations · 3.2k across the 31 of their papers we have counts for
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stat.ML2021★ 2 cited
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
stat.ML2021★ 2 cited
Scalable Cross Validation Losses for Gaussian Process Models
Martin Jankowiak, Geoff Pleiss
We introduce a simple and scalable method for training Gaussian process (GP) models that exploits cross-validation and nearest neighbor truncation. To accommodate binary and multi-…