The workshop for registered participants is complemented by a keynote by Richard Samworth to which everyone interested is invited, no registration necessary!

For more than two centuries, least-squares regression has been a cornerstone of statistical practice, while classical nonparametric smoothing methods have long served as standard tools for analysing complex data.  In this workshop, we will revisit these methods from a modern perspective and ask: Are we making the best possible use of them? Recent work in statistical theory by Richard Samworth and others shows that these familiar methods can often be improved by incorporating additional structural information, such as shape constraints or properties of the underlying error distribution.

Richard Samworth will explore recent developments in distributionally adaptive statistical methods. Richard Samworth is Professor of Statistical Science and Director of the Statistical Laboratory at the University of Cambridge. A Fellow of the Royal Society, he is the recipient of numerous distinctions, including the COPSS Presidents' Award, the David Cox Medal and the Royal Statistical Society Guy Medal in Silver in 2025. His research has made fundamental contributions to nonparametric statistics, statistical learning theory, and high-dimensional methodology, particularly in shape-constrained estimation and adaptive nonparametric procedures. A defining feature of his research is the combination of rigorous theoretical guarantees with methods designed to be computationally efficient and practically applicable.

Richard Samworth will present recent work with Elliot H. Young and Rajen D. Shah on "Outrigger local polynomial regression“. A preprint of the paper is available on arXiv https://arxiv.org/abs/2603.11282.