13 citations · 17 across the 5 of their papers we have counts for
4 papers · 1 filter
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin +2
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained…
Computation-Aware Kalman Filtering and Smoothing
Marvin Pförtner, Jonathan Wenger, Jon Cockayne +1
Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with…
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Marvin Pförtner, Ingo Steinwart, Philipp Hennig +1
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, an…
Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner +2
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. Th…