24 citations · 73 across the 28 of their papers we have counts for
5 papers · 1 filter
Tensor-based second-order causal discovery
Nathan Ouyang, Kexin Wang, Anna Seigal
Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its i…
Multi-context principal component analysis
Kexin Wang, Salil Bhate, João M. Pereira +3
Principal component analysis (PCA) is a tool to capture factors that explain variation in data. Across domains, data are now collected across multiple contexts (for example, indivi…
Linear causal disentanglement via higher-order cumulants
Paula Leyes Carreno, Chiara Meroni, Anna Seigal
Linear causal disentanglement is a recent method in causal representation learning to describe a collection of observed variables via latent variables with causal dependencies betw…
Linear Causal Disentanglement via Interventions
Chandler Squires, Anna Seigal, Salil Bhate +1
Causal disentanglement seeks a representation of data involving latent variables that relate to one another via a causal model. A representation is identifiable if both the latent…
Mixtures and products in two graphical models
Anna Seigal, Guido Montufar
We compare two statistical models of three binary random variables. One is a mixture model and the other is a product of mixtures model called a restricted Boltzmann machine. Altho…