40 citations · 46 across the 4 of their papers we have counts for
6 papers · 1 filter
CLaRe: Compact near-lossless Latent Representations of High-Dimensional Object Data
Emma Zohner, Edward Gunning, Giles Hooker +1
Latent feature representation methods play an important role in the dimension reduction and statistical modeling of high-dimensional complex data objects. However, existing approac…
Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
Kevin Tan, Giles Hooker, Edward L. Ionides
Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed n…
An Understanding of Principal Differential Analysis
Edward Gunning, Giles Hooker
In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary diff…
Interpreting Models via Single Tree Approximation
Yichen Zhou, Giles Hooker
We propose a procedure to build a decision tree which approximates the performance of complex machine learning models. This single approximation tree can be used to interpret and s…
Maximal Autocorrelation Functions in Functional Data Analysis
Giles Hooker, Steven Roberts
This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-represent a functional subspace in terms of directi…
Truncated Linear Models for Functional Data
Peter Hall, Giles Hooker
A conventional linear model for functional data involves expressing a response variable in terms of the explanatory function , via the model: $Y=a+\int_I b(t)X(t)dt+\hbox…