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 Uncertainty Quantification

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

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry

no code implementations • 25 Jun 2020 • Ke Ye, Mu Niu, Pokman Cheung, Zhenwen Dai, YuAn Liu

The introduction of our strip algorithm, tailored for manifolds with extra symmetries, and the ball algorithm, designed for arbitrary manifolds, constitutes our significant contribution.

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