123 citations · 129 across the 9 of their papers we have counts for
10 papers
Emulation strategies for Bayesian inference of regional left ventricle material parameters
Hongjin Ren, Vinny Davies, Hao Gao +2
Patient-specific biomechanical models of the left ventricle can relate cardiac magnetic resonance imaging to regional myocardial material properties, but existing emulator-based st…
Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds
Yizhu Wang, Mu Niu, Xiaochen Yang
We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unkn…
Intrinsic Bayesian Optimisation on Complex Constrained Domain
Yuan Liu, Mu Niu, Claire Miller
Motivated by the success of Bayesian optimisation algorithms in the Euclidean space, we propose a novel approach to construct Intrinsic Bayesian optimisation (In-BO) on manifolds w…
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…
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
Mu Niu, Zhenwen Dai, Neil Lawrence +1
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to descr…
Modelling group dynamic animal movement
Roland Langrock, J. Grant C. Hopcraft, Paul G. Blackwell +7
Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, part…