Jonathan Crabbe
15 papers · 2020–2024 · 4 venues · across top CS/AI venues
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Venues
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Research topics
Machine Learning
(13)
Artificial Intelligence
(8)
Core AI
(8)
Core Methods
(8)
Deep Learning
(6)
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(5)
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(3)
Learning Types Β· Machine Learning
(3)
Optimization & Theory
(2)
Data Science & Analytics
(1)
Bayesian & Probabilistic
(1)
Learning Types Β· Deep Learning
(1)
Methods
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Models
(1)
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(1)
Keywords
feature importance
(4)
latent representation
(2)
neural network
(2)
conformal prediction
(2)
tabular data
(2)
fourier analysis
(1)
statistical independence
(1)
explainable ai
(1)
model interpretability
(1)
geometric deep learning
(1)
uncertainty quantification
(1)
time series
(1)
causal structure
(1)
perturbation analysis
(1)
support vector machine
(1)
feature attribution
(1)
neural network regularization
(1)
copula modeling
(1)
personalized medicine
(1)
density estimation
(1)
Papers
DAGnosis: Localized Identification of Data Inconsistencies using Structures
AISTATS 2024
Time Series Diffusion in the Frequency Domain
ICML 2024
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
NeurIPS 2023
Joint Training of Deep Ensembles Fails Due to Learner Collusion
NeurIPS 2023
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
ICLR 2023
TRIAGE: Characterizing and auditing training data for improved regression
NeurIPS 2023
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
NeurIPS 2023
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
NeurIPS 2022
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
NeurIPS 2022
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
NeurIPS 2022
Data-SUITE: Data-centric identification of in-distribution incongruous examples
ICML 2022
Label-Free Explainability for Unsupervised Models
ICML 2022
Explaining Latent Representations with a Corpus of Examples
NeurIPS 2021
Explaining Time Series Predictions with Dynamic Masks
ICML 2021
Learning outside the Black-Box: The pursuit of interpretable models
NeurIPS 2020