Machine Learning in Infinite Dimensions will be held at the University of Bath from the 5th to the 9th of August 2024.

Lifting high-dimensional problems to an infinite-dimensional space and designing algorithms in that setting has been a fruitful idea in many areas of applied mathematics, including inverse problems, optimisation, and partial differential equations. This approach is sometimes referred to as “optimise-then-discretise” and allows the development of algorithms that are inherently dimension- and discretisation-independent and can perform better in high-dimensions. In the context of machine learning, this paradigm can be rephrased as “learn-then-discretise”.

This workshop aims to bring together researchers who work on different aspects of infinite-dimensionality in machine learning. Topics include, but are not restricted to, Gaussian process regression, operator learning, function spaces of neural networks, and measure transport.

Attendance is free but we ask all attendees to apply before 5th June. Support for travel and/or accommodation is available for (a limited number of) PhD students and early career researchers – please apply before 5th June.

We acknowledge generous support of the International Centre for Mathematical Sciences, Edinburgh and the London Mathematical Society, as well as the EPSRC programme grant Maths4DL.

More details can be found on the workshop’s webpage: https://sites.google.com/view/machine-learning-workshop-bath