Publications and Conferences

  • De Sousa Ribeiro, F. and Glocker, B. (2025) ‘Demystifying variational diffusion models.’ Foundations and Trends in Computer Graphics and Vision, 17(2), pp. 76–170. https://doi:10.1561/0600000113 

    Dhir, A., Sedgwick, R., Kori, A., Glocker, B. and van der Wilk, M. (2025) ‘Continuous Bayesian model selection for multivariate causal discovery.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2411.10154

    Jansma, A., Yao, Y., Wolfe, J., Del Debbio, L, Beentjes, S.V., Ponting, C.P.  and Khamseh, A. (2025) 'High order expression dependencies finely resolve cryptic states and subtypes in single cell data,' Molecular Systems Biology, 21, pp. 173–207. https://doi.org/10.1038/s44320-024-00074-1

    Jones, C., de Sousa Ribeiro, F., Roschewitz, M., Castro, D.C. and Glocker, B. (2024) ‘Rethinking fair representation learning for performance‑sensitive tasks.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2410.04120

    Kori, A., Toni, F. and Glocker, B. (2025) ‘Identifiable Object Representations under Spatial Ambiguities.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2506.07806

    la Torre, I.P., Kelly, D.A., Menéndez, H.D. and Clark, D. (2025) ‘To BEE or Not to BEE: Estimating more than Entropy with Biased Entropy Estimators.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2501.11395

    Rasal, R., Kori, A., De Sousa Ribeiro, F., Xia, T. and Glocker, B. (2025) ‘Diffusion counterfactual generation with semantic abduction.’ Poster in: International Conference on Machine Learning (ICML 2025). arXiv (Cornell University). https://doi.org/10.48550/arXiv.2506.07883

  • Beckers, S., Chockler, H. and Halpern, J.Y. (2024) 'A causal analysis of harm,' Minds andMachines, 34(3). https://doi.org/10.1007/s11023-024-09689-7

    Benitez-Aurioles, J., Joules, A., Brusini, I., Peek, N. and Sperrin, M. (2024) ‘Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.07879 

    Benitez‑Aurioles, J., Wynants, L., Peek, N., Goodley, P., Crosbie, P. and Sperrin, M. (2024) ‘The continuous net benefit: Assessing the clinical utility of prediction models when informing a continuum of decisions.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.07882 

    Chockler, H. and Halpern, J.Y. (2024) ‘Explaining image classifiers.’ In: 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024). pp. 264–272. https://doi:10.24963/kr.2024/25 

    Dutt, R., Bohdal, O., Tsaftaris, S.A. and Hospedales, T. (2023) 'FairTune: Optimizing parameter efficient fine tuning for fairness in medical image analysis,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2310.05055

    Gopinath, K., Hoopes, A., Alexander, D.C., Arnold, S.E., Balbastre, Y., Billot, B., Casamitjana, A., Cheng, Y., Zhi Chua, R.Y., Edlow, B.L., Fischl, B., Gazula, H., Hoffmann, M., Dirk Keene, C., Kim, S., Kimberly, W.T., Laguna, S., Larson, K.E., Leemput, K.V., Puonti, O., Rodrigues, L.M., Rosen, M.S., Tregidgo, H.F.J., Varadarajan, D., Young, S.I., Dalca, A.V. and Iglesias, J.E. (2024) 'Synthetic data in generalizable, learning-based neuroimaging,' Imaging Neuroscience, 2, pp. 1–22. https://doi.org/10.1162/imag_a_00337

    Gultchin, L., Guo, S., Malek, A., Chiappa, S. and Silva, R. (2024) ‘Pragmatic Fairness: Developing Policies with Outcome Disparity Control.’ In: Proceedings of the Third Conference on Causal Learning and Reasoning, 236, pp. 243–264. https://doi:10.48550/arXiv.2301.12278

    Jones, C., Castro, D.C., De Sousa Ribeiro, F., Oktay, O., McCradden, M. and Glocker, B. (2024) 'A causal perspective on dataset bias in machine learning for medical imaging,' Nature Machine Intelligence, 6(2), pp. 138–146. https://doi.org/10.1038/s42256-024-00797-8

    Kori, A., Locatello, F., Santhirasekaram, A., Toni, F., Glocker, B. and De Sousa Ribeiro, F. (2024) ‘Identifiable object‑centric representation learning via probabilistic slot attention.’ In: Thirty‑Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). arXiv:2406.07141. https://doi:10.48550/arXiv.2406.07141

    Lin, L., Poppe, K., Wood, A., Martin, G.P., Peek, N., Sperrin, M. (2024) 'Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care,' Frontiers in Epidemiology, 4. https://doi.org/10.3389/fepid.2024.1326306

    Li, K., Xie, W., Huang, Y., Deng, D., Hong, L., Li, Z., Silva, R. and Zhang, N.L. (2024) 'Dual risk minimization: towards Next-Level robustness in fine-tuning Zero-Shot models,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.19757

    Melistas, T., Spyrou, N., Gkouti, N., Sanchez, P., Vlontzos, A., Panagakis, Y., Papanastasiou, G. and Tsaftaris, S.A. (2024) ‘Benchmarking counterfactual image generation.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2403.20287

    Peng, W., Xia, T., De Sousa Ribeiro, F., Bosschieter, T., Adeli, E., Zhao, Q., Glocker, B. and Pohl, K.M. (2024) ‘Latent 3D Brain MRI Counterfactual.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2409.05585 

    Penn, J., Gunderson, L.M., Bravo‑Hermsdorff, G., Silva, R. and Watson, D.S. (2024) ‘BudgetIV: Optimal partial identification of causal effects with mostly invalid instruments.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2411.06913

    Ploddi, K., Sperrin, M., Martin, G.P. and O’Connell, M.M. (2024) ‘Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2412.04275 

    Pranger, S., Chockler, H., Tappler, M. and Könighofer, B. (2024) ‘Test Where Decisions Matter: Importance‑driven Testing for Deep Reinforcement Learning,’ In: Thirty‑Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). arXiv:2411.07700. https://doi:10.48550/arXiv.2411.07700

    Roschewitz, M., De Sousa Ribeiro, F., Xia, T., Khara, G. and Glocker, B. (2024) 'Counterfactual contrastive learning: robust representations via causal  image synthesis,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.09605

    Roschewitz, M., De Sousa Ribeiro, F., Xia, T., Khara, G. and Glocker, B. (2025) ‘Robust image representations with counterfactual contrastive learning.’ Medical Image Analysis, 79, 102876. https://doi:10.1016/j.media.2024.102876 

    van Geloven, N., Keogh, R.H., van Amsterdam, W., Cinà, G., Krijthe, J.H., Peek, N., Luijken, K., Magliacane, S., Morzywołek, P., van Ommen, T., Putter, H., Sperrin, M., Wang, J., Weir, D.L. and Didelez, V. (2024) ‘The risks of risk assessment: causal blind spots when using prediction models for treatment decisions.’arXiv (Cornell University). https://doi:10.48550/arXiv.2402.17366

    Watson, D.S., Penn, J., Gunderson, L.M., Bravo-Hermsdorff, G., Mastouri, A. and Silva, R. (2024) 'Bounding Causal Effects with Leaky Instruments,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.04446

    Xia, T., Roschewitz, M., De Sousa Ribeiro, F., Jones, C. and Glocker, B. (2024) 'Mitigating attribute amplification in counterfactual image generation,' in Lecture notes in computer science, pp. 546–556. https://doi.org/10.1007/978-3-031-72117-5_51

    Yu, J., Koukorinis, A., Colombo, N., Zhu, Y. and Silva, R. (2024) 'Structured learning of compositional sequential interventions,' arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.05745

    Yu, J., Zhou, Y., He, Y., Zhang, N.L. and Silva, R. (2024) ‘Fine‑Tuning Pre‑trained Language Models for Robust Causal Representation Learning.’ arXiv (Cornell University). https://doi:10.48550/arXiv.2410.14375