8 papers
Posterior uncertainty for kernel density estimates
Dennis Christensen, Torjus Svardal, Leiv Rønneberg +1
Recent work in predictive Bayesian inference has enabled novel Bayesian interpretations of many well-known stochastic one-step-ahead predictive algorithms. In this paper, we study…
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…
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…
Multi-Output Robust and Conjugate 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, MOGPs are sens…
Heterogeneous Clinical Trial Outcomes via Multi-Output Gaussian Processes
Owen Thomas, Leiv Rønneberg
We make use of Kronecker structure for scaling Gaussian Process models to large-scale, heterogeneous, clinical data sets. Repeated measures, commonly performed in clinical research…
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
Leiv Rønneberg, Vidhi Lalchand, Paul D. W. Kirk
Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop a…
FlexKnot and Gaussian Process for 21 cm global signal analysis and foreground separation
Stefan Heimersheim, Leiv Rønneberg, Henry Linton +2
The cosmological 21 cm signal is one of the most promising avenues to study the Epoch of Reionization. One class of experiments aiming to detect this signal is global signal experi…
Composite local low-rank structure in learning drug sensitivity
The Tien Mai, Leiv Rønneberg, Zhi Zhao +2
The molecular characterization of tumor samples by multiple omics data sets of different types or modalities (e.g. gene expression, mutation, CpG methylation) has become an invalua…