Learning outside the Black-Box: The pursuit of interpretable models. - Jonathan Crabbé; Yao Zhang; William R. Zame; Mihaela van der Schaar (NeurIPS, 2020)

Explaining Time Series Predictions with Dynamic Masks. - Jonathan Crabbé; Mihaela van der Schaar (ICML, 2021)

Explaining Latent Representations with a Corpus of Examples. - Jonathan Crabbé; Zhaozhi Qian; Fergus Imrie; Mihaela van der Schaar (NeurIPS, 2021)

Label-Free Explainability for Unsupervised Models. - Jonathan Crabbé; Mihaela van der Schaar (ICML, 2022)

Data-SUITE: Data-centric identification of in-distribution incongruous examples. - Nabeel Seedat; Jonathan Crabbé; Mihaela van der Schaar (ICML, 2022)

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. - Jonathan Crabbé; Alicia Curth; Ioana Bica; Mihaela van der Schaar (NeurIPS, 2022)

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. - Jonathan Crabbé; Mihaela van der Schaar (NeurIPS, 2022)

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. - Nabeel Seedat; Jonathan Crabbé; Ioana Bica; Mihaela van der Schaar (NeurIPS, 2022)

DAUX: a Density-based Approach for Uncertainty eXplanations. - Hao Sun 017; Boris van Breugel; Jonathan Crabbé; Nabeel Seedat; Mihaela van der Schaar (CoRR, 2022)

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. - Alan Jeffares; Tennison Liu; Jonathan Crabbé; Fergus Imrie; Mihaela van der Schaar (ICLR, 2023)

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models. - Arthur Vandenhoeke; Lennert Antson; Guillem Ballesteros; Jonathan Crabbé; Michal Shimoni (IGARSS, 2023)

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. - Jonathan Crabbé; Mihaela van der Schaar (NeurIPS, 2023)

Joint Training of Deep Ensembles Fails Due to Learner Collusion. - Alan Jeffares; Tennison Liu; Jonathan Crabbé; Mihaela van der Schaar (NeurIPS, 2023)

TRIAGE: Characterizing and auditing training data for improved regression. - Nabeel Seedat; Jonathan Crabbé; Zhaozhi Qian; Mihaela van der Schaar (NeurIPS, 2023)

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. - Hao Sun 017; Boris van Breugel; Jonathan Crabbé; Nabeel Seedat; Mihaela van der Schaar (NeurIPS, 2023)

Robust multimodal models have outlier features and encode more concepts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (CoRR, 2023)

MatterGen: a generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Sasha Shysheya; Jonathan Crabbé; Lixin Sun; Jake Smith; Ryota Tomioka; Tian Xie 002 (CoRR, 2023)

DAGnosis: Localized Identification of Data Inconsistencies using Structures. - Nicolas Huynh; Jeroen Berrevoets; Nabeel Seedat; Jonathan Crabbé; Zhaozhi Qian; Mihaela van der Schaar (AISTATS, 2024)

Time Series Diffusion in the Frequency Domain. - Jonathan Crabbé; Nicolas Huynh; Jan Stanczuk; Mihaela van der Schaar (ICML, 2024)

LaTable: Towards Large Tabular Models. - Boris van Breugel; Jonathan Crabbé; Rob Davis; Mihaela van der Schaar (CoRR, 2024)

Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (Trans. Mach. Learn. Res., 2024)

A generative model for inorganic materials design. - Claudio Zeni; Robert Pinsler; Daniel Zügner; Andrew Fowler; Matthew Horton; Xiang Fu 005; Zilong Wang; Aliaksandra Shysheya; Jonathan Crabbé; Shoko Ueda; Roberto Sordillo; Lixin Sun; et al. (Nat., 2025)