6 citations · 8 across the 4 of their papers we have counts for
6 papers
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +3
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealin…
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…
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Frederik Warburg, Martin Jørgensen, Javier Civera +1
Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncer…
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…
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…