151 citations · 692 across the 40 of their papers we have counts for
45 papers
Differentiable Programming for Differential Equations: A Review
Facundo Sapienza, Jordi Bolibar, Frank Schäfer +8
The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Ma…
A Generic Approach for Reproducible Model Distillation
Yunzhe Zhou, Peiru Xu, Giles Hooker
Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "…
The Infinitesimal Jackknife and Combinations of Models
Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker
The Infinitesimal Jackknife is a general method for estimating variances of parametric models, and more recently also for some ensemble methods. In this paper we extend the Infinit…
S-LIME: Stabilized-LIME for Model Explanation
Zhengze Zhou, Giles Hooker, Fei Wang
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are…
Generalised Boosted Forests
Indrayudh Ghosal, Giles Hooker
This paper extends recent work on boosting random forests to model non-Gaussian responses. Given an exponential family our goal is to obtain an est…
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning
Lucas Mentch, Giles Hooker
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures. Twenty years later this division feels far more ephemeral, both in terms of assi…