18 citations · 60 across the 6 of their papers we have counts for
7 papers
Quasiconformal model with CNN features for large deformation image registration
Ho Law, Gary P. T. Choi, Ka Chun Lam +1
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical mod…
Decomposition of Longitudinal Deformations via Beltrami Descriptors
Ho Law, Lok Ming Lui, Chun Yin Siu
We present a mathematical model to decompose a longitudinal deformation into normal and abnormal components. The goal is to detect and extract subtle quivers from periodic motions…
Hyperparameter Learning via Distributional Transfer
Ho Chung Leon Law, Peilin Zhao, Lucian Chan +2
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We pro…
A Differentially Private Kernel Two-Sample Test
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic +1
Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those d…
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron +4
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of…
Bayesian Approaches to Distribution Regression
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic +1
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than…
Testing and Learning on Distributions with Symmetric Noise Invariance
Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic
Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing…