| Learning overlapping representations for the estimation of individualized treatment effects Y Zhang, A Bellot, M Schaar AISTATS 2020, 2020 | 118 | 2020 |
| Predicting the risk of inpatient hypoglycemia with machine learning using electronic health records Y Ruan, A Bellot, Z Moysova, GD Tan, A Lumb, J Davies, ... Diabetes care 43 (7), 1504-1511, 2020 | 80 | 2020 |
| Miracle: Causally-aware imputation via learning missing data mechanisms T Kyono, Y Zhang, A Bellot, M van der Schaar NeurIPS 2021, 2021 | 65 | 2021 |
| Conditional independence testing using generative adversarial networks A Bellot, M van der Schaar NeurIPS 2019, 2019 | 61 | 2019 |
| Continuous-time modeling of counterfactual outcomes using neural controlled differential equations N Seedat, F Imrie, A Bellot, Z Qian, M van der Schaar ICML 2022, 2022 | 55 | 2022 |
| Neural graphical modelling in continuous-time: consistency guarantees and algorithms A Bellot, K Branson, M van der Schaar ICLR 2022, 2021 | 48* | 2021 |
| Accounting for unobserved confounding in domain generalization A Bellot, M van der Schaar arXiv preprint arXiv:2007.10653, 2020 | 48* | 2020 |
| Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably TE Cowling, DA Cromwell, A Bellot, LD Sharples, J van der Meulen Journal of Clinical Epidemiology 133, 43-52, 2021 | 31 | 2021 |
| Learning dynamic and personalized comorbidity networks from event data using deep diffusion processes Z Qian, A Alaa, A Bellot, M Schaar, J Rashbass AISTATS 2020, 2020 | 27 | 2020 |
| Tree-based bayesian mixture model for competing risks A Bellot, M Schaar AISTATS 2018, 2018 | 24 | 2018 |
| Multitask boosting for survival analysis with competing risks A Bellot, M van der Schaar NeurIPS 2018, 2018 | 23 | 2018 |
| Policy Analysis using Synthetic Controls in Continuous-Time A Bellot, M van der Schaar ICML 2021, 2021 | 20 | 2021 |
| Boosted trees for risk prognosis A Bellot, M van der Schaar Machine Learning for Healthcare Conference, 2-16, 2018 | 19 | 2018 |
| Boosting transfer learning with survival data from heterogeneous domains A Bellot, M van der Schaar AISTATS 2019, 2019 | 18 | 2019 |
| A hierarchical bayesian model for personalized survival predictions A Bellot, M Van der Schaar IEEE journal of biomedical and health informatics 23 (1), 72-80, 2018 | 18 | 2018 |
| Generalization bounds and algorithms for estimating conditional average treatment effect of dosage A Bellot, A Dhir, G Prando arXiv preprint arXiv:2205.14692, 2022 | 16 | 2022 |
| Linear deconfounded score method: scoring DAGs with dense unobserved confounding A Bellot, M van der Schaar IEEE Transactions on Neural Networks and Learning Systems 35 (4), 4948-4962, 2024 | 13* | 2024 |
| Flexible modelling of longitudinal medical data: A Bayesian nonparametric approach A Bellot, MVD Schaar ACM Transactions on Computing for Healthcare 1 (1), 1-15, 2020 | 11 | 2020 |
| One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data TE Cowling, A Bellot, J Boyle, K Walker, A Kuryba, S Galbraith, ... British Journal of Cancer 123 (10), 1474-1480, 2020 | 8 | 2020 |
| Towards bounding causal effects under Markov equivalence A Bellot UAI 2024, 2024 | 5 | 2024 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteEmail được xác minh tại ee.ucla.edu