146 citations · 264 across the 8 of their papers we have counts for
8 papers
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes
Seth D. Axen, Alexandra Gessner, Christian Sommer +2
Paleoclimatology -- the study of past climate -- is relevant beyond climate science itself, such as in archaeology and anthropology for understanding past human dispersal. Informat…
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner +9
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential…
High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Although it is widely known that Gaussian processes can be conditioned on observations of the gradient, this functionality is of limited use due to the prohibitive computational co…
Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig +2
Riemannian manifolds provide a principled way to model nonlinear geometric structure inherent in data. A Riemannian metric on said manifolds determines geometry-aware shortest path…
Three-dimensional Models of Core-collapse Supernovae From Low-mass Progenitors With Implications for Crab
G. Stockinger, H. -Th. Janka, D. Kresse +9
We present 3D full-sphere supernova simulations of non-rotating low-mass (~9 Msun) progenitors, covering the entire evolution from core collapse through bounce and shock revival, t…
Integrals over Gaussians under Linear Domain Constraints
Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
Integrals of linearly constrained multivariate Gaussian densities are a frequent problem in machine learning and statistics, arising in tasks like generalized linear models and Bay…