publications

2026

1. EJS

   Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases

   Charita Dellaporta, and Theodoros Damoulas

   Electronic Journal of Statistics, 2026

   DOI arXiv Bib PDF

   @article{dellaporta2023robust,
     title = {Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases},
     author = {Dellaporta, Charita and Damoulas, Theodoros},
     journal = {Electronic Journal of Statistics},
     year = {2026},
     volume = {20},
     number = {1},
     pages = {445--502},
     doi = {10.1214/26-EJS2487},
   }

2. Under review

   Amortised and provably-robust simulation-based inference

   Ayush Bharti^*, Charita Dellaporta^*, Yuga Hikida, and François-Xavier Briol

   arXiv preprint arXiv:2602.11325, 2026

   Bib PDF

   @article{bharti2026amortised,
     title = {Amortised and provably-robust simulation-based inference},
     author = {Bharti, Ayush and Dellaporta, Charita and Hikida, Yuga and Briol, Fran{\c{c}}ois-Xavier},
     journal = {arXiv preprint arXiv:2602.11325},
     year = {2026},
   }

2025

1. ICML

   Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets

   Charita Dellaporta^*, Patrick O’Hara^*, and Theodoros Damoulas

   International Conference on Machine Learning, 2025

   arXiv Bib PDF

   @article{dellaporta2024decision,
     title = {Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets},
     author = {Dellaporta, Charita and O'Hara, Patrick and Damoulas, Theodoros},
     journal = {International Conference on Machine Learning},
     pages = {13011-13043},
     year = {2025},
     organization = {PMLR},
   }

2. Preprint

   Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets

   Charita Dellaporta^*, Patrick O’Hara^*, and Theodoros Damoulas

   arXiv preprint arXiv:2505.03585, 2025

   arXiv Bib PDF

   @article{dellaporta2025decision,
     title = {Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets},
     author = {Dellaporta, Charita and O'Hara, Patrick and Damoulas, Theodoros},
     journal = {arXiv preprint arXiv:2505.03585},
     year = {2025},
   }

3. Under review

   Total Robustness in Bayesian Nonlinear Regression for Measurement Error Problems under Model Misspecification

   Mengqi Chen, Charita Dellaporta, Thomas B Berrett, and Theodoros Damoulas

   arXiv preprint arXiv:2510.03131, 2025

   arXiv Bib PDF

   @article{chen2025total,
     title = {Total Robustness in Bayesian Nonlinear Regression for Measurement Error Problems under Model Misspecification},
     author = {Chen, Mengqi and Dellaporta, Charita and Berrett, Thomas B and Damoulas, Theodoros},
     journal = {arXiv preprint arXiv:2510.03131},
     year = {2025},
   }

2024

1. NeurIPS BDU

   Distributionally Robust Optimisation with Bayesian Ambiguity Sets

   Charita Dellaporta^*, Patrick O’Hara^*, and Theodoros Damoulas

   NeurIPS workshop in Bayesian Decision-making and Uncertainty, 2024

   arXiv Bib PDF

   @article{dellaporta2024distributionally,
     title = {Distributionally Robust Optimisation with Bayesian Ambiguity Sets},
     author = {Dellaporta, Charita and O'Hara, Patrick and Damoulas, Theodoros},
     journal = {NeurIPS workshop in Bayesian Decision-making and Uncertainty},
     year = {2024},
   }

2022

1. AISTATS

   Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap

   Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, and François-Xavier Briol

   In International Conference on Artificial Intelligence and Statistics, 2022

   arXiv Bib PDF Code

   @inproceedings{dellaporta2022robust,
     title = {Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap},
     author = {Dellaporta, Charita and Knoblauch, Jeremias and Damoulas, Theodoros and Briol, Fran{\c{c}}ois-Xavier},
     booktitle = {International Conference on Artificial Intelligence and Statistics},
     pages = {943--970},
     year = {2022},
     organization = {PMLR},
   }