13 citations · 37 across the 13 of their papers we have counts for
8 papers · 1 filter
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid, Xingchen Wan, Martin Jørgensen +2
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks. Furthermore, exist…
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Grégoire Clarté, Martin Jørgensen +1
In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. H…
Bézier Gaussian Processes for Tall and Wide Data
Martin Jørgensen, Michael A. Osborne
Modern approximations to Gaussian processes are suitable for "tall data", with a cost that scales well in the number of observations, but under-performs on ``wide data'', scaling p…
Last Layer Marginal Likelihood for Invariance Learning
Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober +1
Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model…
Reparametrization Invariance in non-parametric Causal Discovery
Martin Jørgensen, Søren Hauberg
Causal discovery estimates the underlying physical process that generates the observed data: does X cause Y or does Y cause X? Current methodologies use structural conditions to tu…
Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni
We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasi…
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen, Søren Hauberg
We present a probabilistic model where the latent variable respects both the distances and the topology of the modeled data. The model leverages the Riemannian geometry of the gene…
Reliable training and estimation of variance networks
Nicki S. Detlefsen, Martin Jørgensen, Søren Hauberg
We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks. We derive a locally aware mini-batching scheme…