Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

We present a regression technique for data-driven problems based on polynomial chaos
expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ) …

In many scientific and engineering applications, we are tasked with the optimisation of an
expensive to evaluate black box function $\func $. Traditional methods for this problem …

There is currently a large gap in performance between the statistically rigorous methods like
linear regression or additive splines and the powerful deep methods using neural networks …

In observational studies, identification of ATEs is generally achieved by assuming that the
correct set of confounders has been measured and properly included in the relevant models …

In many scientific and engineering applications, we are tasked with the maximisation of an
expensive to evaluate black box function f. Traditional settings for this problem assume just …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

Bolukbasi et al.(2016) presents one of the first gender bias mitigation techniques for word
embeddings. Their method takes pre-trained word embeddings as input and attempts to …

Random feature methods have been successful in various machine learning tasks, are easy
to compute, and come with theoretical accuracy bounds. They serve as an alternative …

Sparse shrunk additive models and sparse random feature models have been developed
separately as methods to learn low-order functions, where there are few interactions …