207 citations · 303 across the 20 of their papers we have counts for
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stat.ML2018★ 8 cited
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens, Kieran R. Campbell, Christopher Yau
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. Howeve…
stat.ML2018★ 1 cited
TensOrMachine: Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris C. Holmes, Christopher Yau
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra. Here,…