| Differentially private diffusion models generate useful synthetic images S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2023 | 71 | 2023 |
| On Locality of Local Explanation Models S Ghalebikesabi, L Ter-Minassian, K Diaz-Ordaz, C Holmes 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 42 | 2021 |
| Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness S Ghalebikesabi, R Cornish, LJ Kelly, C Holmes Proceedings of the 24th International Conference on Artificial Intelligence …, 2021 | 23* | 2021 |
| Mitigating Statistical Bias within Differentially Private Synthetic Data S Ghalebikesabi, H Wilde, J Jewson, A Doucet, S Vollmer, C Holmes Uncertainty in Artificial Intelligence, 696-705, 2022 | 18* | 2022 |
| A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods VD Wild, S Ghalebikesabi, D Sejdinovic, J Knoblauch 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 15 | 2023 |
| AirGapAgent: Protecting privacy-conscious conversational agents E Bagdasarian, R Yi, S Ghalebikesabi, P Kairouz, M Gruteser, S Oh, ... Proceedings of the 2024 on ACM SIGSAC Conference on Computer and …, 2024 | 11* | 2024 |
| AIRIVA: a deep generative model of adaptive immune repertoires MF Pradier, N Prasad, P Chapfuwa, S Ghalebikesabi, M Ilse, ... Machine Learning for Healthcare Conference, 588-611, 2023 | 10 | 2023 |
| Soham De, Samuel L S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes Smith, Olivia Wiles, and Borja Balle. Differentially private diffusion …, 2023 | 10 | 2023 |
| Operationalizing contextual integrity in privacy-conscious assistants S Ghalebikesabi, E Bagdasaryan, R Yi, I Yona, I Shumailov, A Pappu, ... arXiv preprint arXiv:2408.02373, 2024 | 8 | 2024 |
| Differentially Private Statistical Inference through -Divergence One Posterior Sampling J Jewson, S Ghalebikesabi, C Holmes 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 7 | 2023 |
| Differentially Private Diffusion Models Generate Useful Synthetic Images (2023) S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2020 | 5 | 2020 |
| A Quasi-Bayesian Nonparametric Density Estimator via Autoregressive Predictive Updates S Ghalebikesabi, C Holmes, E Fong, B Lehmann Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence …, 2022 | 4* | 2022 |
| CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data Z Cheng, D Wan, M Abueg, S Ghalebikesabi, R Yi, E Bagdasarian, ... arXiv preprint arXiv:2409.13903, 2024 | 2 | 2024 |
| Challenges and Opportunities of Shapley values in a Clinical Context L Ter-Minassian, S Ghalebikesabi, K Diaz-Ordaz, C Holmes ICML 2022 Workshop on Interpretable Machine Learning in Healthcare, 2023 | 2 | 2023 |
| Explainable AI for survival analysis: a median-SHAP approach L Ter-Minassian, S Ghalebikesabi, K Diaz-Ordaz, C Holmes arXiv preprint arXiv:2402.00072, 2024 | 1 | 2024 |
| A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis F Falck, X Zhu, S Ghalebikesabi, M Kormaksson, M Vandemeulebroecke, ... Journal of Biomedical Informatics 154, 104641, 2024 | | 2024 |
| Generative modelling: addressing open problems in model misspecification and differential privacy S Ghalebikesabi University of Oxford, 2023 | | 2023 |
| Identification of Underlying Disease Domains by Longitudinal Latent Factor Analysis for Secukinumab Treated Patients in Psoriatic Arthritis and Rheumatoid Arthritis Trials … X Zhu, F Falck, S Ghalebikesabi, M Kormaksson, M Vandemeulebroecke, ... Arthritis & Rheumatology 73, 2516-2518, 2021 | | 2021 |
| A Tutorial on Model Explainability S Ghalebikesabi | | |
Chris Holmes確認したメール アドレス: stats.ox.ac.uk
