We are glad to inform you that our next speaker is
Kate
Lee(University of Auckland), who will talk about "
Towards Robust and Scalable Bayesian Learning" on the
29th May at 10am UK time (note the different time!), with an abstract reported below.
Meeting ID: 382 746 871 856 192
Passcode: FZ2gn73K
Speaker: Kate
Lee (University of Auckland)
Title: Towards Robust and Scalable Bayesian Learning
Abstract: My talk consists of two parts. The first focuses on Generalised Bayesian Inference (GBI), where the loss hyperparameters are critical under model misspecification yet difficult to determine in a principled way. We introduce a Bayesian
framework for hyperparameter inference using held-out data, enabling coherent estimation and uncertainty quantification, together with amortized generalized-posterior approximations that avoid repeated costly sampling across datasets and hyperparameter values.
The second addresses scalability in simulation-based inference through data reduction, developing methods that learn informative low-dimensional summaries to enable efficient inference in complex settings, with applications to gravitational wave analysis.
Keywords: GBI, SBI, amortized learning
We're looking forward to seeing you next week,
best,
Massimiliano on the behalf of the OWABI Seminar Organisers
------
Dr. Massimiliano Tamborrino SFHEA
Reader (Associate Professor)
Department of Statistics
WIHEA Fellow and Internationalisation Learning Circle Co-Lead