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Found 16 papers, 12 papers with code
Hodge-Compositional Edge Gaussian Processes
We propose principled Gaussian processes (GPs) for modeling functions defined over the edge set of a simplicial 2-complex, a structure similar to a graph in which edges may form triangular faces.
Implicit Manifold Gaussian Process Regression
Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets.
Intrinsic Gaussian Vector Fields on Manifolds
Various applications ranging from robotics to climate science require modeling signals on non-Euclidean domains, such as the sphere.
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification.
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.
On power sum kernels on symmetric groups
In this note, we introduce a family of "power sum" kernels and the corresponding Gaussian processes on symmetric groups $\mathrm{S}_n$.
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.
Quadric Hypersurface Intersection for Manifold Learning in Feature Space
The knowledge that data lies close to a particular submanifold of the ambient Euclidean space may be useful in a number of ways.
Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer.
Matérn Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties.
Matérn Gaussian processes on Riemannian manifolds
Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance.
Efficiently Sampling Functions from Gaussian Process Posteriors
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
