Jacobsen, Joern-Henrik

17 publications

ICML 2025 Addressing Misspecification in Simulation-Based Inference Through Data-Driven Calibration Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Joern-Henrik Jacobsen, Marco Cuturi

ICLRW 2023 Considerations for Distribution Shift Robustness in Health Arno Blaas, Andrew Miller, Luca Zappella, Joern-Henrik Jacobsen, Christina Heinze-Deml

NeurIPSW 2023 Inferring Cardiovascular Biomarkers with Hybrid Model Learning Ortal Senouf, Jens Behrmann, Joern-Henrik Jacobsen, Pascal Frossard, Emmanuel Abbe, Antoine Wehenkel

TMLR 2023 Robust Hybrid Learning with Expert Augmentation Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen

CLeaR 2022 Learning Invariant Representations with Missing Data Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller

AISTATS 2021 Understanding and Mitigating Exploding Inverses in Invertible Neural Networks Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Joern-Henrik Jacobsen

ICML 2021 Environment Inference for Invariant Learning Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel

NeurIPSW 2021 Learning Invariant Representations with Missing Data Mark Goldstein, Joern-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew Miller

ICML 2021 Out-of-Distribution Generalization via Risk Extrapolation (REx) David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

ICML 2020 Fundamental Tradeoffs Between Invariance and Sensitivity to Adversarial Perturbations Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen

ICML 2020 How to Train Your Neural ODE: The World of Jacobian and Kinetic Regularization Chris Finlay, Joern-Henrik Jacobsen, Levon Nurbekyan, Adam Oberman

ICML 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models Without Sampling Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel

ICLR 2019 Excessive Invariance Causes Adversarial Vulnerability Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge

ICML 2019 Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

ICML 2019 Invertible Residual Networks Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen

NeurIPS 2019 Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse, Joern-Henrik Jacobsen

NeurIPS 2019 Residual Flows for Invertible Generative Modeling Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen