Showing stat.MLShow all
3 papers · 1 filter
stat.ML2024
Residual Deep Gaussian Processes on Manifolds
Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy
We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary l…
stat.ML2023
Implicit Manifold Gaussian Process Regression
Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause +1
Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with h…
stat.ML2023
Intrinsic Gaussian Vector Fields on Manifolds
Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy
Various applications ranging from robotics to climate science require modeling signals on non-Euclidean domains, such as the sphere. Gaussian process models on manifolds have recen…