4 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…