341 citations · 355 across the 10 of their papers we have counts for
11 papers
Atlas Gaussian processes on restricted domains and point clouds
Mu Niu, Yue Zhang, Ke Ye +3
In real-world applications, data often reside in restricted domains with unknown boundaries, or as high-dimensional point clouds lying on a lower-dimensional, nontrivial, unknown m…
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
Mu Niu, Zhenwen Dai, Pokman Cheung +1
This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics (GPUM) in point clouds. In many real…
Extrinsic Bayesian Optimizations on Manifolds
Yihao Fang, Mu Niu, Pokman Cheung +1
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective fu…
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
Ke Ye, Mu Niu, Pokman Cheung +2
Amidst the growing interest in nonparametric regression, we address a significant challenge in Gaussian processes(GP) applied to manifold-based predictors. Existing methods primari…
Intrinsic Gaussian processes on complex constrained domains
Mu Niu, Pokman Cheung, Lizhen Lin +3
We propose a class of intrinsic Gaussian processes (in-GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irr…
Extrinsic Gaussian processes for regression and classification on manifolds
Lizhen Lin, Mu Niu, Pokman Cheung +1
Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although…