Accepted Papers
Papers listed here do not constitute a proceedings for this workshop.
(Poster 1) Bayesian data selection [paper]
Eli N Weinstein and Jeffrey Miller
(Poster 2) Uncertainty estimation under model misspecification in neural network regression [paper]
Maria R. Cervera, Rafael Dätwyler, Francesco D'Angelo, Hamza Keurti, Benjamin F. Grewe, and Christian Henning
(Poster 3) Fast approximate BayesBag model selection via Taylor expansions [paper]
Neil A Spencer and Jeffrey Miller
(Poster 4) Diversity and generalization in neural network ensembles [paper]
Luis Antonio Ortega, Andrés Masegosa, and Rafael Cabañas
(Poster 5) A shared parameter model accounting for drop-out not at random in a predictive model for systolic bloodpressure using the HUNT study [paper]
Aurora C Hofman, Ingelin Steinsland, and Emma M. L. Ingeström
(Poster 6) Influential observations in Bayesian regression tree models [paper]
Matthew Pratola Ed George, and Rob McCulloch
(Poster 7) Invariant priors for Bayesian quadrature [paper]
Masha Naslidnyk, Javier Gonzalez, and Maren Mahsereci
(Poster 8) Composite goodness-of-fit tests with kernels [paper]
Oscar Key, Tamara Fernandez, Arthur Gretton, and Francois-Xavier Briol
(Poster 9) Inferior clusterings in misspecified Gaussian mixture models [paper]
Siva Rajesh Kasa and Vaibhav Rajan
(Poster 10) Blindness of score-based methods to isolated components and mixing proportions [paper]
Li K Wenliang and Heishiro Kanagawa
(Poster 11) Bounding Wasserstein distance with couplings [paper]
Niloy Biswas and Lester Mackey
(Poster 12) Relaxing the I.I.D. assumption: Adaptively minimax optimal regret via root-entropic regularization [paper]
Blair Bilodeau, Jeffrey Negrea, and Daniel M. Roy
(Poster 13) Measuring the sensitivity of Gaussian processes to kernel choice [paper]
William T Stephenson, Soumya Ghosh, Tin D Nguyen, Mikhail Yurochkin, Sameer Deshpande, and Tamara Broderick
(Poster 14) Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap [paper]
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, and Francois-Xavier Briol
(Poster 15) Your bandit model is not perfect: Introducing robustness to restless bandits enabled by deep reinforcement learning [paper]
Jackson Killian, Lily Xu, Arpita Biswas, and Milind Tambe
(Poster 16) Bayesian calibration of imperfect computer models using physics-informed priors [paper]
Michail Spitieris and Ingelin Steinsland
(Poster 17) Forcing a model to be correct for classification [paper]
Jiae Kim and Steven MacEachern
(Poster 18) Make cross-validation Bayes again [paper]
Yuling Yao and Aki Vehtari
(Poster 19) Evaluating Bayesian hierarchical models for sc-RNA seq data [paper]
Sijia Li, Martín López-García, and Luisa Cutillo
(Poster 20) On robustness of counterfactuals in structural models [paper]
Yaroslav Mukhin
(Poster 21) Robust generalised Bayesian inference for intractable likelihoods [paper]
Takuo Matsubara, Jeremias Knoblauch, Francois-Xavier Briol, and Chris Oates
(Poster 22) Boosting heterogeneous VAEs via multi-objective optimization [paper]
Adrian Javaloy, Maryam Meghdadi, and Isabel Valera
(Poster 23) PAC^m-Bayes: Narrowing the empirical risk gap in the misspecified Bayesian regime [paper]
Warren R Morningstar, Alex Alemi, and Joshua V Dillon