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.
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.
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.
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.