Dear all, the next OneWorld Approximate Bayesian Inference seminar www.warwick.ac.uk/owabi <http://www.warwick.ac.uk/owabi> is quickly approaching! We are delighted to announce that our next speaker is Larry Wasserman <https://www.stat.cmu.edu/~larry/><https://www.stat.cmu.edu/~larry/>(Carnegie Mellon University) who will talk about "/Robust Simulation Based Inference/" on *Wednesday the 25^th  March at 2pm Uk time* (note the different day and time!) with an abstract reported below. The talk will be streamed at the OWABI MS Team channel General | OWABI Seminar: One World Approximate Bayesian Inference Seminar | Microsoft Teams <https://teams.microsoft.com/l/team/19%3AdhZ_4e_XLNJzCXPAMzTvT6BZ5KShEETkd_wtTY52VI81%40thread.tacv2/conversations?groupId=9c061d11-f88c-4cee-938f-bf40e7393879&tenantId=09bacfbd-47ef-4465-9265-3546f2eaf6bc> at the following link *Join: *_https://teams.microsoft.com/meet/3560358333600?p=Klx01mfhGgKETxxz6K <https://teams.microsoft.com/meet/3560358333600?p=Klx01mfhGgKETxxz6K>_ Meeting ID: 356 035 833 360 0 Passcode: Fe3m7ty9 *Speaker*: Larry Wasserman <https://www.stat.cmu.edu/~larry/> (Carnegie Mellon University) *Title*: Robust Simulation Based Inference *Abstract*: Simulation-Based Inference (SBI) is an approach to statistical inference where simulations from an assumed model are used to construct estimators and confidence sets. SBI is often used when the likelihood is intractable and to construct confidence sets that do not rely on asymptotic methods or regularity conditions. Traditional SBI methods assume that the model is correct, but, as always, this can lead to invalid inference when the model is misspecified. This paper introduces robust methods that allow for valid frequentist inference in the presence of model misspecification. We propose a framework where the target of inference is a projection parameter that minimizes a discrepancy between the true distribution and the assumed model. The method guarantees valid inference, even when the model is incorrectly specified and even if the standard regularity conditions fail. Alternatively, we introduce model expansion through exponential tilting as another way to account for model misspecification. We also develop an SBI based goodness-of-fit test to detect model misspecification. Finally, we propose two ideas that are useful in the SBI framework beyond robust inference: an SBI based method to obtain closed form approximations of intractable models and an active learning approach to more efficiently sample the parameter space. *Keywords*: Exponential tilting, model misspecification, robust inference, simulation based inference, valid inference. *Reference*: L. Tomaselli, V. Ventura, L. Wasserman. Robust Simulation Based Inference.  Preprint at ArXiv:2508.02404, <https://arxiv.org/pdf/2508.02404>2025. Best, Umberto Picchini on the behalf of the OWABI Organisers -- ____________________________________________________________________ Umberto Picchini, Full Professor of Mathematical Statistics https://umbertopicchini.github.io/ , Bluesky: @upicchini.bsky.social