| How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models A Alaa, B Van Breugel, ES Saveliev, M van der Schaar International Conference on Machine Learning, 290-306, 2022 | 227 | 2022 |
| DECAF: Generating fair synthetic data using causally-aware generative networks B Van Breugel, T Kyono, J Berrevoets, M Van der Schaar Advances in Neural Information Processing Systems 34, 22221-22233, 2021 | 119 | 2021 |
| Stereotype and skew: Quantifying gender bias in pre-trained and fine-tuned language models DV Manela, D Errington, T Fisher, B van Breugel, P Minervini arXiv preprint arXiv:2101.09688, 2021 | 104 | 2021 |
| Membership Inference Attacks against Synthetic Data through Overfitting Detection B van Breugel, H Sun, Z Qian, M van der Schaar Proceedings of the 26th International Conference on Artificial Intelligence …, 2023 | 48 | 2023 |
| Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data B van Breugel, Z Qian, M van der Schaar Proceedings of the 40th International Conference on Machine Learning 202 …, 2023 | 39 | 2023 |
| Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data B van Breugel, M van der Schaar arXiv preprint arXiv:2304.03722, 2023 | 28 | 2023 |
| Why Tabular Foundation Models Should Be a Research Priority B van Breugel, M van der Schaar International Conference on Machine Learning, 2024 | 26 | 2024 |
| Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes N Seedat, N Huynh, B van Breugel, M van der Schaar Forty-first International Conference on Machine Learning, 2024 | 22* | 2024 |
| Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data B van Breugel, N Seedat, F Imrie, M van der Schaar Advances in Neural Information Processing Systems 36, 2023 | 19 | 2023 |
| How faithful is your synthetic data AM Alaa, B van Breugel, E Saveliev, M van der Schaar Sample-level Metrics for Evaluating and Auditing Generative Models, 2022 | 14 | 2022 |
| What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization H Sun, B van Breugel, J Crabbé, N Seedat, M van der Schaar Advances in Neural Information Processing Systems 36, 2023 | 9* | 2023 |
| Synthetic data in biomedicine via generative artificial intelligence B van Breugel, T Liu, D Oglic, M van der Schaar Nature Reviews Bioengineering 2 (12), 991-1004, 2024 | 6 | 2024 |
| RadEdit: stress-testing biomedical vision models via diffusion image editing F Pérez-García, S Bond-Taylor, PP Sanchez, B van Breugel, DC Castro, ... European Conference on Computer Vision, 2024 | 6 | 2024 |
| Decaf: generating fair synthetic data using causally-aware generative networks T Kyono, B van Breugel, J Berrevoets, M van der Schaar NeurIPS. cc, 1-13, 2021 | 3 | 2021 |
| Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models Y Li, B van Breugel, M van der Schaar International Conference on Learning Representations, 2024 | 2 | 2024 |
| Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes T Liu, AJ Chan, B van Breugel, M van der Schaar NeurIPS 2022 Workshop on Algorithmic Fairness through the Lens of Causality …, 2022 | 2 | 2022 |
| LaTable: Towards Large Tabular Models B van Breugel, J Crabbé, R Davis, M van der Schaar arXiv preprint arXiv:2406.17673, 2024 | | 2024 |
| The Spherical Grasshopper Problem B van Breugel arXiv preprint arXiv:2307.05359, 2023 | | 2023 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteПідтверджена електронна адреса в ee.ucla.edu