3 papers
stat.ME2025
Conformalized Bayesian Inference, with Applications to Random Partition Models
Nicola Bariletto, Nhat Ho, Alessandro Rinaldo
Bayesian posterior distributions naturally represent parameter uncertainty informed by data. However, when the parameter space is complex, as in many nonparametric settings where i…
stat.ML2024
Data-Driven DRO and Economic Decision Theory: An Analytical Synthesis With Bayesian Nonparametric Advancements
Nicola Bariletto, Khai Nguyen, Nhat Ho
We develop an analytical synthesis that bridges data-driven Distributionally Robust Optimization (DRO) and Economic Decision Theory under Ambiguity (DTA). By reinterpreting standar…
stat.ML2024
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization
Nicola Bariletto, Nhat Ho
Training machine learning and statistical models often involves optimizing a data-driven risk criterion. The risk is usually computed with respect to the empirical data distributio…