6 papers
Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination
Mengqi Chen, Thomas B. Berrett, Theodoros Damoulas +1
Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While…
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…
Rates of Convergence of Generalised Variational Inference Posteriors under Prior Misspecification
Terje Mildner, Paris Giampouras, Theodoros Damoulas
We prove rates of convergence and robustness to prior misspecification within a Generalised Variational Inference (GVI) framework with bounded divergences. This addresses a signifi…
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…
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…
Routing on Sparse Graphs with Non-metric Costs for the Prize-collecting Travelling Salesperson Problem
Patrick O'Hara, M. S. Ramanujan, Theodoros Damoulas
In many real-world routing problems, decision makers must optimise over sparse graphs such as transportation networks with non-metric costs on the edges that do not obey the triang…