5 citations · 9 across the 4 of their papers we have counts for
4 papers
Causal Network Discovery from Interventional Count Data with Latent Linear DAGs
Yijiao Zhang, Hongzhe Li
The increasing availability of interventional data offers new opportunities for causal discovery, with gene perturbation studies providing a prominent example. Such data are typica…
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian +3
Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal…
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian +2
Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and ch…
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders
Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal
The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the eve…