Program

Thursday June 20

09:30 Registration
10:00 Welcome

10:15 Peter Eichelsbacher: Mean field models and exchangeability due to de Finetti and due to Stein
10:45 Bhaswar Bhattacharya: Higher-Order Graphon Theory: Fluctuations, Inference, and Degeneracies
11:15 Hanna Doering: Limit Theorems for crossing number and stress of the projected random geometric graph

11:45 – 13:30 Lunch and posters in the department

13:30 Takuo Matsubara: Generalised Bayesian Inference for Discrete Intractable Likelihood
14:00 Alessandro Barp: Controlling convergence with Stein kernels
14:30 Heishiro Kanagawa: Controlling Moments with Kernel Stein Discrepancies

15:00-15:30 Coffee break

15:30 Robert Gaunt: Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator
16:00 Andreas Anastasiou: Wasserstein distance bounds on the normal approximation of empirical autocovariances and cross-covariances under non-stationarity and stationarity
16:30 Mikolaj Kasprzak: A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces

Friday June 21

09:00 Ivan Nourdin: Quantitative CLT for deep neural networks — the one-dimensional case
09:30 Giovanni Peccati: Quantitative CLTs for neural networks -- the functional case
10:00 Xiaochuan Yang: On the boundary effect of some stochastic geometric models

10:30 – 11:00 Coffee break

11:00 Matthias Schulte: Stein's method for the Dickman distribution
11:30 Chinmoy Bhattcharjee: Sharp noise stability in continuum percolations via Spectra of Poisson functionals

12:00 – 13:00 Lunch and posters in the department

13:00 Bruno Ebner: On a new resampling method for unimodality testing via the empirical zero bias transform
13:30 Wenkai Xu: Stein's method for assessing and generating graphs
14:00 Yvik Swan: Normal approximation for standardized posteriors in exponential families

14:30 Closing