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2020 – today
- 2024
[c16]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. AISTATS 2024: 1864-1872- [c15]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. ICML 2024 - [i18]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. CoRR abs/2402.05933 (2024) - [i17]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. CoRR abs/2402.17599 (2024) - [i16]Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar:
LaTable: Towards Large Tabular Models. CoRR abs/2406.17673 (2024) - 2023
- [c14]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. ICLR 2023 - [c13]Arthur Vandenhoeke, Lennert Antson, Guillem Ballesteros, Jonathan Crabbé, Michal Shimoni:
Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models. IGARSS 2023: 958-961 - [c12]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. NeurIPS 2023 - [c11]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. NeurIPS 2023 - [c10]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. NeurIPS 2023 - [c9]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. NeurIPS 2023 - [i15]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. CoRR abs/2301.11323 (2023) - [i14]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. CoRR abs/2303.05506 (2023) - [i13]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. CoRR abs/2304.06715 (2023) - [i12]Jonathan Crabbé, Pau Rodríguez, Vaishaal Shankar, Luca Zappella, Arno Blaas:
Robust multimodal models have outlier features and encode more concepts. CoRR abs/2310.13040 (2023) - [i11]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. CoRR abs/2310.18970 (2023) - [i10]Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie:
MatterGen: a generative model for inorganic materials design. CoRR abs/2312.03687 (2023) - 2022
- [c8]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. ICML 2022: 4391-4420 - [c7]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. ICML 2022: 19467-19496 - [c6]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS 2022 - [c5]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. NeurIPS 2022 - [c4]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. NeurIPS 2022 - [i9]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. CoRR abs/2202.08836 (2022) - [i8]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. CoRR abs/2203.01928 (2022) - [i7]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR abs/2206.08363 (2022) - [i6]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
DAUX: a Density-based Approach for Uncertainty eXplanations. CoRR abs/2207.05161 (2022) - [i5]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. CoRR abs/2209.11222 (2022) - [i4]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. CoRR abs/2210.13043 (2022) - 2021
- [c3]Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. ICML 2021: 2166-2177 - [c2]Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar:
Explaining Latent Representations with a Corpus of Examples. NeurIPS 2021: 12154-12166 - [i3]Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. CoRR abs/2106.05303 (2021) - [i2]Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar:
Explaining Latent Representations with a Corpus of Examples. CoRR abs/2110.15355 (2021) - 2020
- [c1]Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar:
Learning outside the Black-Box: The pursuit of interpretable models. NeurIPS 2020 - [i1]Jonathan Crabbé
, Yao Zhang, William R. Zame, Mihaela van der Schaar:
Learning outside the Black-Box: The pursuit of interpretable models. CoRR abs/2011.08596 (2020)
Coauthor Index
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last updated on 2024-09-04 01:24 CEST by the dblp team
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