Real-world optimisation problems often feature complex combinations of (...
The signature kernel is a positive definite kernel for sequential data. ...
In ℝ^d, it is well-known that cumulants provide an alternative to
moment...
Batch Bayesian optimisation and Bayesian quadrature have been shown to b...
We analyze the Nyström approximation of a positive definite kernel
assoc...
Given a probability measure μ on a set 𝒳 and a vector-valued
function φ,...
Calculation of Bayesian posteriors and model evidences typically require...
Convolutional layers within graph neural networks operate by aggregating...
Many forecasts consist not of point predictions but concern the evolutio...
We provide explicit bounds on the number of sample points required to
es...
We study kernel quadrature rules with positive weights for probability
m...
We work with continuous-time, continuous-space stochastic dynamical syst...
Stochastic differential equations (SDEs) are a staple of mathematical
mo...
We study the classical problem of recovering a multidimensional source
p...
For a d-dimensional random vector X, let p_n, X be the probability
that ...
In this paper, we consider the underdamped Langevin diffusion (ULD) and
...
Sequential data such as time series, video, or text can be challenging t...
We propose a new technique to accelerate algorithms based on Gradient De...
Given a discrete probability measure supported on N atoms and a set of n...
The sequence of so-called signature moments describes the laws of many
s...
We introduce a Bayesian approach to learn from stream-valued data by usi...
The normalized sequence of moments characterizes the law of any
finite-d...
We introduce a new feature map for barcodes that arise in persistent hom...
Predictive modelling and supervised learning are central to modern data
...
We introduce features for massive data streams. These stream features ca...
We present a novel framework for kernel learning with sequential data of...