Joern-Henrik Jacobsen
15 papers · 2019–2025 · 5 venues · across top CS/AI venues
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Achievements
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π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (21) π£ Hot Topic Early Bird π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint
π
Triple Crown
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Conference Pioneer
β‘
Prolific Year Β· Productive
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Century Club Β· Bronze
ποΈ
Keyword Collector
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Research topics
Keywords
domain generalization
(3)
generative model
(3)
adversarial robustness
(3)
normalizing flow
(2)
maximum likelihood
(2)
optimal transport
(2)
adversarial example
(2)
energy-based model
(2)
invertible residual network
(2)
density estimation
(2)
distribution shift
(2)
out-of-distribution generalization
(1)
adversarial training
(1)
robust classification
(1)
algorithmic fairness
(1)
representation learning
(1)
disentangled representation
(1)
model robustness
(1)
gradient computation
(1)
covariate shift
(1)
Papers
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
ICML 2025
Learning Invariant Representations with Missing Data
CLeaR 2022
Environment Inference for Invariant Learning
ICML 2021
Out-of-Distribution Generalization via Risk Extrapolation (REx)
ICML 2021
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
AISTATS 2021
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
ICML 2020
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
ICML 2020
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
ICML 2020
Understanding the Limitations of Conditional Generative Models
ICLR 2020
Your classifier is secretly an energy based model and you should treat it like one
ICLR 2020
Excessive Invariance Causes Adversarial Vulnerability
ICLR 2019
Flexibly Fair Representation Learning by Disentanglement
ICML 2019
Invertible Residual Networks
ICML 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
NeurIPS 2019
Residual Flows for Invertible Generative Modeling
NeurIPS 2019