Jonathan Crabbé

According to our database, Jonathan Crabbé authored at least 20 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

LaTable: Towards Large Tabular Models.

Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar

CoRR, 2024

Time Series Diffusion in the Frequency Domain.

Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DAGnosis: Localized Identification of Data Inconsistencies using Structures.

Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

MatterGen: a generative model for inorganic materials design.

Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie

CoRR, 2023

Robust multimodal models have outlier features and encode more concepts.

Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas

CoRR, 2023

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization.

Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

TRIAGE: Characterizing and auditing training data for improved regression.

Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Joint Training of Deep Ensembles Fails Due to Learner Collusion.

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance.

Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models.

Arthur Vandenhoeke, Lennert Antson, Guillem Ballesteros, Jonathan Crabbé, Michal Shimoni

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization.

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

DAUX: a Density-based Approach for Uncertainty eXplanations.

Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

CoRR, 2022

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data.

Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations.

Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability.

Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data-SUITE: Data-centric identification of in-distribution incongruous examples.

Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

Label-Free Explainability for Unsupervised Models.

Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

2021

Explaining Latent Representations with a Corpus of Examples.

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Explaining Time Series Predictions with Dynamic Masks.

Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Learning outside the Black-Box: The pursuit of interpretable models.

Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020