Jonathan Crabbe
15 papers · 2020–2024 · 4 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (4) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12)
🐣
Hot Topic Early Bird
🤝
Dynamic Duo
(15)
🗃️
Keyword Collector
(55)
❓
The Questioner
⚡
Prolific Year
(5)
💎
Century Club
(15)
🔥
Unstoppable
(5)
Conferences
NIPS (9)
ICML (4)
AISTATS (1)
ICLR (1)
Top co-authors
Keywords
feature importance
(4)
neural network
(2)
conformal prediction
(2)
feature attribution
(1)
perturbation analysis
(1)
geometric deep learning
(1)
time series
(1)
statistical independence
(1)
data quality
(1)
causal structure
(1)
latent space
(1)
copula modeling
(1)
support vector machine
(1)
latent representation
(1)
personalized medicine
(1)
model interpretability
(1)
training dynamics
(1)
treatment effect estimation
(1)
explainable ai
(1)
representation learning
(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
NIPS 2023
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
NIPS 2023
Joint Training of Deep Ensembles Fails Due to Learner Collusion
NIPS 2023
TRIAGE: Characterizing and auditing training data for improved regression
NIPS 2023
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
ICLR 2023
Label-Free Explainability for Unsupervised Models
ICML 2022
Data-SUITE: Data-centric identification of in-distribution incongruous examples
ICML 2022
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
NIPS 2022
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
NIPS 2022
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
NIPS 2022
Explaining Latent Representations with a Corpus of Examples
NIPS 2021
Explaining Time Series Predictions with Dynamic Masks
ICML 2021
Learning outside the Black-Box: The pursuit of interpretable models
NIPS 2020