Publications

Fawkes, J. et al. (2025) “Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition”, in Proceedings of Machine Learning Research, pp. 1423–1431.

Potaptchik, P., Azangulov, I. and Deligiannidis, G. (2025) “Linear Convergence of Diffusion Models Under the Manifold Hypothesis”, in Proceedings of Machine Learning Research.

Clerico, E. et al. (2025) “Generalisation under gradient descent via deterministic PAC-Bayes”, in Proceedings of Machine Learning Research, pp. 349–389.

Wang, Z. and Holmes, C. (2025) “On Subjective Uncertainty Quantification and Calibration in Natural Language Generation”, in Proceedings of Machine Learning Research, pp. 3799–3807.

Alfano, C. et al. (2025) “LEARNING MIRROR MAPS IN POLICY MIRROR DESCENT”, in 13th International Conference on Learning Representations Iclr 2025, pp. 60369–60387.

Ter-Minassian, L. et al. (2025) “Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation”, in Proceedings of Machine Learning Research, pp. 1900–1908.

Adams, J. et al. (2025) “Individualised Counterfactual Examples Using Conformal Prediction Intervals”, in Proceedings of Machine Learning Research, pp. 425–444.

Vary, S., Martínez-Rubio, D. and Rebeschini, P. (2024) “Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization”, arXiv.

Hung, E., Mantziou, A. and Reinert, G. (2024) “A Bayesian mixture model for Poisson network autoregression.”

Deligiannidis, G. et al. (2024) “On importance sampling and independent Metropolis-Hastings with an unbounded weight function”, arXiv.