24 citations · 73 across the 28 of their papers we have counts for
36 papers
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…
Causal discovery under mean independence and linearity
Geert Mesters, Alvaro Ribot, Anna Seigal +1
Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumption is fragile: shared volatili…
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…
Beyond independent component analysis: identifiability and algorithms
Alvaro Ribot, Anna Seigal, Piotr Zwiernik
Independent Component Analysis (ICA) is a classical method for recovering latent variables with useful identifiability properties. For independent variables, cumulant tensors are d…
Multi-subspace power method for decomposing partially symmetric tensors
Kexin Wang, João M. Pereira, Joe Kileel +1
We present an algorithm for low rank decomposition of tensors of any symmetry type, from fully asymmetric to fully symmetric. It recovers the decomposition one summand at a time vi…
Orthogonal eigenvectors and singular vectors of tensors
Alvaro Ribot, Anna Seigal, Piotr Zwiernik
The spectral theorem says that a real symmetric matrix has an orthogonal basis of eigenvectors and that, for a matrix with distinct eigenvalues, the basis is unique (up to signs).…