36 citations · 39 across the 8 of their papers we have counts for
12 papers
A computationally-tractable measure of global sensitivity for sampling-based Bayesian inference
Arina Odnoblyudova, Charita Dellaporta, François-Xavier Briol
Bayesian inference can often be sensitive to the choice of hyperparameters of the prior or likelihood, yet defining and quantifying this sensitivity in a principled and computation…
Conjugate Generalized Bayesian Inference for Discrete Doubly Intractable Problems
William Laplante, Matias Altamirano, Jeremias Knoblauch +2
Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Baye…
Kernel Quantile Embeddings and Associated Probability Metrics
Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol +1
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistic…
On the Robustness of Kernel Goodness-of-Fit Tests
Xing Liu, François-Xavier Briol
Goodness-of-fit testing is often criticized for its lack of practical relevance: since ``all models are wrong'', the null hypothesis that the data conform to our model is ultimatel…
Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff +5
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models.…
Robust and Conjugate Gaussian Process Regression
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and s…