4 citations · 4 across the 8 of their papers we have counts for
9 papers
Conservative neural posterior estimation via distributionally robust training
William Laplante, Yuga Hikida, Charita Dellaporta +2
Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose…
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…
Amortised and provably-robust simulation-based inference
Ayush Bharti, Charita Dellaporta, Yuga Hikida +1
Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outli…
Total robustness in Bayesian Nonlinear Regression
Mengqi Chen, Charita Dellaporta, Thomas B. Berrett +1
Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution…
Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas
Distributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical d…
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas
Decision making under uncertainty is challenging as the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior beliefs…
Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas
Decision making under uncertainty is challenging since the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior belie…
Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases
Charita Dellaporta, Theodoros Damoulas
Measurement error occurs when a covariate influencing a response variable is corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accu…
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas +1
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena…