20 citations · 37 across the 4 of their papers we have counts for
4 papers
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Suman Ravuri, Mélanie Rey, Shakir Mohamed +1
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model cl…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…
Learning to Induce Causal Structure
Nan Rosemary Ke, Silvia Chiappa, Jane Wang +7
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms o…
Copula Mixture Model for Dependency-seeking Clustering
Melanie Rey, Volker Roth
We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the g…