| 2024 | AISTATS | DAGnosis: Localized Identification of Data Inconsistencies using Structures. | Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabb, Zhaozhi Qian, Mihaela van der Schaar |
| 2024 | ICML | Time Series Diffusion in the Frequency Domain. | Jonathan Crabb, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar |
| 2023 | ICLR | TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. | Alan Jeffares, Tennison Liu, Jonathan Crabb, Fergus Imrie, Mihaela van der Schaar |
| 2023 | IGARSS | Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models. | Arthur Vandenhoeke, Lennert Antson, Guillem Ballesteros, Jonathan Crabb, Michal Shimoni |
| 2022 | ICML | Label-Free Explainability for Unsupervised Models. | Jonathan Crabb, Mihaela van der Schaar |
| 2022 | ICML | Data-SUITE: Data-centric identification of in-distribution incongruous examples. | Nabeel Seedat, Jonathan Crabb, Mihaela van der Schaar |
| 2021 | ICML | Explaining Time Series Predictions with Dynamic Masks. | Jonathan Crabb, Mihaela van der Schaar |