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Ruby Sedgwick

2 accepted papers

2025

Continuous Bayesian Model Selection for Multivariate Causal Discovery

ICML 2025poster

Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not hold in real-world applications leading to a loss of guarantees and poor performance in practice. Recent work has shown…

Cited by 1SourcePDFScholar
2025

Weighted Sum of Gaussian Process Latent Variable Models

AISTATS 2025poster

This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) for the case where each data point comprises the weighted sum of a known number of…

Cited by 0SourceScholar