Just saw a presentation of Dellaporta et al. (2022).
I am not sure how any of the results are specific to that very impressive paper, but she attributes prior work to Fong, Lyddon, and Holmes (2019); Lyddon, Walker, and Holmes (2018); Matsubara et al. (2021); Pacchiardi and Dutta (2022); Schmon, Cannon, and Knoblauch (2021).
Combines bootstrap, Bayes nonparametrics, MMD, simulation based inference in an M-open setting.
Clearly there is some interesting stuff going on here.
Perhaps this introductory post will be a good start: Generalizing Bayesian Inference.
Dellaporta, Charita, Jeremias Knoblauch, Theodoros Damoulas, and François-Xavier Briol. 2022. “Robust Bayesian Inference for Simulator-Based Models via the MMD Posterior Bootstrap.” arXiv:2202.04744 [Cs, Stat]
Fong, Edwin, Simon Lyddon, and Chris Holmes. 2019. “Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap.” arXiv:1902.03175 [Cs, Stat]
Galvani, Marta, Chiara Bardelli, Silvia Figini, and Pietro Muliere. 2021. “A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap.” Algorithms
14 (1): 11.
Lyddon, Simon, Stephen Walker, and Chris Holmes. 2018. “Nonparametric Learning from Bayesian Models with Randomized Objective Functions.”
In Proceedings of the 32nd International Conference on Neural Information Processing Systems
, 2075–85. NIPS’18. Red Hook, NY, USA: Curran Associates Inc.
Matsubara, Takuo, Jeremias Knoblauch, François-Xavier Briol, and Chris J. Oates. 2021. “Robust Generalised Bayesian Inference for Intractable Likelihoods.” arXiv:2104.07359 [Math, Stat]
Pacchiardi, Lorenzo, and Ritabrata Dutta. 2022. “Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators.” arXiv:2104.03889 [Stat]
Schmon, Sebastian M., Patrick W. Cannon, and Jeremias Knoblauch. 2021. “Generalized Posteriors in Approximate Bayesian Computation.” arXiv:2011.08644 [Stat]