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stat.ML2026
Stepwise Variational Inference with Vine Copulas
Elisabeth Griesbauer, Leiv Rønneberg, Arnoldo Frigessi +2
We propose stepwise variational inference (VI) with vine copulas: a universal VI procedure that combines vine copulas with a novel stepwise estimation procedure of the variational…
stat.ML2026
Dirichlet Scale Mixture Priors for Bayesian Neural Networks
August Arnstad, Leiv Rønneberg, Geir Storvik
Neural networks are the cornerstone of modern machine learning, yet can be difficult to interpret, give overconfident predictions and are vulnerable to adversarial attacks. Bayesia…
stat.ML2025
Outlier-Robust Multi-Output Gaussian Processes
Joshua Rooijakkers, Leiv Rønneberg, François-Xavier Briol +2
Multi-output Gaussian process (MOGP) regression allows modelling dependencies among multiple correlated response variables. Similarly to standard Gaussian processes (GPs), MOGPs ar…