Jonathan Crabbé

About Jonathan Crabbé

Jonathan Crabbé, With an exceptional h-index of 8 and a recent h-index of 8 (since 2020), a distinguished researcher at University of Cambridge, specializes in the field of Interpretability, Generative AI, Robust Machine Learning, Representation Learning, Time Series.

His recent articles reflect a diverse array of research interests and contributions to the field:

DAGnosis: Localized Identification of Data Inconsistencies using Structures

Time Series Diffusion in the Frequency Domain

Mattergen: a generative model for inorganic materials design

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

TRIAGE: Characterizing and auditing training data for improved regression

Robust multimodal models have outlier features and encode more concepts

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance

Jonathan Crabbé Information

University

Position

DAMTP

Citations(all)

184

Citations(since 2020)

184

Cited By

1

hIndex(all)

8

hIndex(since 2020)

8

i10Index(all)

7

i10Index(since 2020)

7

Email

University Profile Page

Google Scholar

Jonathan Crabbé Skills & Research Interests

Interpretability

Generative AI

Robust Machine Learning

Representation Learning

Time Series

Top articles of Jonathan Crabbé

DAGnosis: Localized Identification of Data Inconsistencies using Structures

arXiv preprint arXiv:2402.17599

2024/2/26

Time Series Diffusion in the Frequency Domain

arXiv preprint arXiv:2402.05933

2024/2/8

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

Mattergen: a generative model for inorganic materials design

arXiv preprint arXiv:2312.03687

2023/12/6

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

2023/11/2

TRIAGE: Characterizing and auditing training data for improved regression

2023/10/29

Robust multimodal models have outlier features and encode more concepts

arXiv preprint arXiv:2310.13040

2023/10/19

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Explaining the Absorption Features of Deep Learning Hyperspectral Classification Models

2023/7/16

Guillem Ballesteros
Guillem Ballesteros

H-Index: 7

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance

2023/4/13

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

Joint Training of Deep Ensembles Fails Due to Learner Collusion

Advances in Neural Information Processing Systems

2024/2/13

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

2023

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

2022/10/24

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

2022/9/22

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

2022/6/16

Label-Free Explainability for Unsupervised Models

2022/3/3

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

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

2022/2/17

Explaining Latent Representations with a Corpus of Examples

2021/10/28

Explaining Time Series Predictions with Dynamic Masks

2021/6/9

Jonathan Crabbé
Jonathan Crabbé

H-Index: 1

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

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

2020

See List of Professors in Jonathan Crabbé University(University of Cambridge)

Co-Authors

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