Jonathan Crabbé

36 papers A* 14A 1C 1Journal 20
YearRankTypeTitle / Venue / Authors
2025 J jnl
A generative model for inorganic materials design.
Nat.
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
2024 A conf
DAGnosis: Localized Identification of Data Inconsistencies using Structures.
AISTATS
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
2024 J jnl
DAGnosis: Localized Identification of Data Inconsistencies using Structures.
CoRR
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
2024 J jnl
Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts.
Trans. Mach. Learn. Res.
Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas
2024 J jnl
LaTable: Towards Large Tabular Models.
CoRR
Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar
2024 A* conf
Time Series Diffusion in the Frequency Domain.
ICML
Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar
2024 J jnl
Time Series Diffusion in the Frequency Domain.
CoRR
Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar
2023 A* conf
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance.
NeurIPS
Jonathan Crabbé, Mihaela van der Schaar
2023 J jnl
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance.
CoRR
Jonathan Crabbé, Mihaela van der Schaar
2023 C conf
Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models.
IGARSS
Arthur Vandenhoeke, Lennert Antson, Guillem Ballesteros, Jonathan Crabbé, Michal Shimoni
2023 A* conf
Joint Training of Deep Ensembles Fails Due to Learner Collusion.
NeurIPS
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar
2023 J jnl
Joint Training of Deep Ensembles Fails Due to Learner Collusion.
CoRR
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar
2023 J jnl
MatterGen: a generative model for inorganic materials design.
CoRR
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
2023 J jnl
Robust multimodal models have outlier features and encode more concepts.
CoRR
Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas
2023 A* conf
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization.
ICLR
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar
2023 J jnl
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization.
CoRR
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar
2023 A* conf
TRIAGE: Characterizing and auditing training data for improved regression.
NeurIPS
Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
2023 J jnl
TRIAGE: Characterizing and auditing training data for improved regression.
CoRR
Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
2023 A* conf
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization.
NeurIPS
Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar
2022 A* conf
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability.
NeurIPS
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
2022 J jnl
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability.
CoRR
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
2022 A* conf
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations.
NeurIPS
Jonathan Crabbé, Mihaela van der Schaar
2022 J jnl
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations.
CoRR
Jonathan Crabbé, Mihaela van der Schaar
2022 J jnl
DAUX: a Density-based Approach for Uncertainty eXplanations.
CoRR
Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar
2022 A* conf
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data.
NeurIPS
Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar
2022 J jnl
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data.
CoRR
Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar
2022 A* conf
Data-SUITE: Data-centric identification of in-distribution incongruous examples.
ICML
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
2022 J jnl
Data-SUITE: Data-centric identification of in-distribution incongruous examples.
CoRR
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
2022 A* conf
Label-Free Explainability for Unsupervised Models.
ICML
Jonathan Crabbé, Mihaela van der Schaar
2022 J jnl
Label-Free Explainability for Unsupervised Models.
CoRR
Jonathan Crabbé, Mihaela van der Schaar
2021 A* conf
Explaining Latent Representations with a Corpus of Examples.
NeurIPS
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
2021 J jnl
Explaining Latent Representations with a Corpus of Examples.
CoRR
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
2021 A* conf
Explaining Time Series Predictions with Dynamic Masks.
ICML
Jonathan Crabbé, Mihaela van der Schaar
2021 J jnl
Explaining Time Series Predictions with Dynamic Masks.
CoRR
Jonathan Crabbé, Mihaela van der Schaar
2020 A* conf
Learning outside the Black-Box: The pursuit of interpretable models.
NeurIPS
Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar
2020 J jnl
Learning outside the Black-Box: The pursuit of interpretable models.
CoRR
Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar