Upcoming Events

Friday 28 August 2026, 10:00, IF room G.03

Speaker: Futoshi Futami (University of Osaka and RIKEN AIP, https://sites.google.com/view/futoshifutami/home)

Title: A Short Tutorial on PAC-Bayes: Basic Ideas and Recent Topics

Abstract: This talk provides a short tutorial on the basic ideas of PAC-Bayes theory. I will explain its main principles and its connections to Bayesian inference and variational Bayes. I will then discuss several recent topics, including information-theoretic generalisation bounds, applications to variational autoencoders, and extensions to sequential decision-making problems.

Biography: Futoshi Futami is an associate professor at the graduate school of engineering science, the University of Osaka , and is also affiliated with the RIKEN AIP. His research focuses on the theoretical foundations of machine learning, including PAC-Bayes theory, information-theoretic analysis, and methods for uncertainty quantification.

Friday 28 August 2026, 11:00, IF room G.03

Speaker: Wenkai Xu (University of Warwick, https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/xuw/)

Title: A Kernel Nonconformity Score for Multivariate Conformal Prediction

Abstract: Conformal prediction has recently attracted attention in post-learning inference. In this talk, we ll start by revisiting some basics on the topic. We then introduce a recent development to construct conformal region for multi-variate outputs. A Multivariate Kernel Score (MKS) will be discussed and we show that the proposed score resembles the Gaussian process posterior variance, unifying Bayesian uncertainty quantification with the coverage guarantees of frequentist-type. Moreover, we demonstrate that the MKS reduces the volume of prediction regions significantly compared to ellipsoidal baselines and conclude by some ongoing and future directions in the topic.

Biography: Wenkai Xu is currently an assistant professor of statistics and machine learning at the department of statistics, University of Warwick. He is also a visiting researcher at Graduate School of Frontier Sciences, University of Tokyo; and Tuebingen AI Center. His research interests includes hypothesis testing, Stein's method, network analysis, and information-theoretical approaches for statistics and machine learning.