Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

no code implementations • NeurIPS 2023 • Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang

We use the the probabilistic metric tensor to simulate Brownian Motion paths on the unknown manifold.

Gaussian Processes

Extrinsic Bayesian Optimizations on Manifolds

no code implementations • 21 Dec 2022 • Yihao Fang, Mu Niu, Pokman Cheung, Lizhen Lin

We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds.

Bayesian Optimization Gaussian Processes +1

Heat kernel and intrinsic Gaussian processes on manifolds

no code implementations • 25 Jun 2020 • Ke Ye, Mu Niu, Pokman Cheung

In this work, we propose an intrinsic approach of constructing the Gaussian process on \if more \fi general manifolds \if {\color{red} in the matrix Lie groups} \fi such as orthogonal groups, unitary groups, Stiefel manifolds and Grassmannian manifolds.

Gaussian Processes regression

Intrinsic Gaussian processes on complex constrained domains

no code implementations • 3 Jan 2018 • Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson

in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.

Gaussian Processes valid