| Explaining Time Series Predictions with Dynamic Masks J Crabbé, M van der Schaar Proceedings of the 38th International Conference on Machine Learning, 2021 | 78 | 2021 |
| Mattergen: a generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ... arXiv preprint arXiv:2312.03687, 2023 | 50 | 2023 |
| Explaining Latent Representations with a Corpus of Examples J Crabbé, Z Qian, F Imrie, M van der Schaar Proceedings of the 35th International Conference on Neural Information …, 2021 | 40 | 2021 |
| Concept Activation Regions: A Generalized Framework For Concept-Based Explanations J Crabbé, M van der Schaar Proceedings of the 36th International Conference on Neural Information …, 2022 | 38 | 2022 |
| Learning outside the Black-Box: The pursuit of interpretable models J Crabbe, Y Zhang, W Zame, M van der Schaar Proceedings of the 34th International Conference on Neural Information …, 2020 | 27 | 2020 |
| Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data N Seedat, J Crabbé, I Bica, M van der Schaar Proceedings of the 36th International Conference on Neural Information …, 2022 | 24 | 2022 |
| Label-Free Explainability for Unsupervised Models J Crabbé, M van der Schaar Proceedings of the 39th International Conference on Machine Learning, 2022 | 22 | 2022 |
| Data-SUITE: Data-centric identification of in-distribution incongruous examples N Seedat, J Crabbe, M van der Schaar Proceedings of the 39th International Conference on Machine Learning, 2022 | 20 | 2022 |
| TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization A Jeffares, T Liu, J Crabbé, F Imrie, M van der Schaar The Eleventh International Conference on Learning Representations, 2023 | 18 | 2023 |
| Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability J Crabbé, A Curth, I Bica, M van der Schaar Proceedings of the Neural Information Processing Systems Track on Datasets …, 2022 | 16 | 2022 |
| Joint Training of Deep Ensembles Fails Due to Learner Collusion A Jeffares, T Liu, J Crabbé, M van der Schaar Proceedings of the 37th International Conference on Neural Information …, 2023 | 12 | 2023 |
| 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 Proceedings of the 37th International Conference on Neural Information …, 2023 | 9 | 2023 |
| TRIAGE: Characterizing and auditing training data for improved regression N Seedat, J Crabbé, Z Qian, M van der Schaar Proceedings of the 37th International Conference on Neural Information …, 2023 | 5 | 2023 |
| Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance J Crabbé, M van der Schaar Proceedings of the 37th International Conference on Neural Information …, 2023 | 5 | 2023 |
| Time series diffusion in the frequency domain J Crabbé, N Huynh, J Stanczuk, M van der Schaar arXiv preprint arXiv:2402.05933, 2024 | 3 | 2024 |
| DAGnosis: Localized Identification of Data Inconsistencies using Structures N Huynh, J Berrevoets, N Seedat, J Crabbé, Z Qian, M van der Schaar International Conference on Artificial Intelligence and Statistics, 1864-1872, 2024 | 2 | 2024 |
| LaTable: Towards Large Tabular Models B van Breugel, J Crabbé, R Davis, M van der Schaar arXiv preprint arXiv:2406.17673, 2024 | | 2024 |
| Robust multimodal models have outlier features and encode more concepts J Crabbé, P Rodríguez, V Shankar, L Zappella, A Blaas arXiv preprint arXiv:2310.13040, 2023 | | 2023 |
| Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models A Vandenhoeke, L Antson, G Ballesteros, J Crabbé, M Shimoni IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium …, 2023 | | 2023 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteE-mailová adresa ověřena na: ee.ucla.edu
