Jörn-Henrik Jacobsen

46 papers A* 13A 2Journal 28Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. ICML Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn-Henrik Jacobsen, Marco Cuturi
2025	J	`jnl`	Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference. CoRR Jens Behrmann, Maria R. Cervera, Antoine Wehenkel, Andrew C. Miller, Albert Cerussi, Pranay Jain, Vivek Venugopal, Shijie Yan, Guillermo Sapiro, Luca Pegolotti, Jörn-Henrik Jacobsen
2024	J	`jnl`	Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. CoRR Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen
2024	J	`jnl`	Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. CoRR Arno Blaas, Adam Golinski, Andrew C. Miller, Luca Zappella, Jörn-Henrik Jacobsen, Christina Heinze-Deml
2024	J	`jnl`	Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers. CoRR Laura Manduchi, Antoine Wehenkel, Jens Behrmann, Luca Pegolotti, Andy C. Miller, Ozan Sener, Marco Cuturi, Guillermo Sapiro, Jörn-Henrik Jacobsen
2023	J	`jnl`	Robust Hybrid Learning With Expert Augmentation. Trans. Mach. Learn. Res. Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen
2023	J	`jnl`	Simulation-based Inference for Cardiovascular Models. CoRR Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen
2022	—	`conf`	Learning Invariant Representations with Missing Data. CLeaR Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
2022	J	`jnl`	Robust Hybrid Learning With Expert Augmentation. CoRR Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen
2021	A*	`conf`	Environment Inference for Invariant Learning. ICML Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
2021	J	`jnl`	Learning Invariant Representations with Missing Data. CoRR Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller
2021	A*	`conf`	Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville
2021	A	`conf`	Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. AISTATS Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
2020	J	`jnl`	Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	J	`jnl`	Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. CoRR Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
2020	A*	`conf`	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
2020	J	`jnl`	Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
2020	A*	`conf`	How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. ICML Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
2020	J	`jnl`	How to train your neural ODE. CoRR Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman
2020	A*	`conf`	Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. ICML Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	J	`jnl`	Out-of-Distribution Generalization via Risk Extrapolation (REx). CoRR David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville
2020	J	`jnl`	Shortcut Learning in Deep Neural Networks. CoRR Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	Shortcut learning in deep neural networks. Nat. Mach. Intell. Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	Understanding and mitigating exploding inverses in invertible neural networks. CoRR Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen
2020	A*	`conf`	Understanding the Limitations of Conditional Generative Models. ICLR Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel
2020	A*	`conf`	Your classifier is secretly an energy based model and you should treat it like one. ICLR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019	J	`jnl`	Conditional Generative Models are not Robust. CoRR Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel
2019	—	`conf`	Excessive Invariance Causes Adversarial Vulnerability. ICLR (Poster) Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge
2019	J	`jnl`	Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot
2019	A*	`conf`	Flexibly Fair Representation Learning by Disentanglement. ICML Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Flexibly Fair Representation Learning by Disentanglement. CoRR Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	A*	`conf`	Invertible Residual Networks. ICML Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen
2019	A*	`conf`	Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. NeurIPS Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
2019	J	`jnl`	Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. CoRR Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen
2019	A*	`conf`	Residual Flows for Invertible Generative Modeling. NeurIPS Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019	J	`jnl`	Residual Flows for Invertible Generative Modeling. CoRR Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019	J	`jnl`	Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2018	J	`jnl`	Excessive Invariance Causes Adversarial Vulnerability. CoRR Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge
2018	J	`jnl`	Invertible Residual Networks. CoRR Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2018	—	`conf`	i-RevNet: Deep Invertible Networks. ICLR (Poster) Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon
2018	J	`jnl`	i-RevNet: Deep Invertible Networks. CoRR Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon
2017	A	`conf`	Dynamic Steerable Blocks in Deep Residual Networks. BMVC Jörn-Henrik Jacobsen, Bert De Brabandere, Arnold W. M. Smeulders
2017	J	`jnl`	Dynamic Steerable Blocks in Deep Residual Networks. CoRR Jörn-Henrik Jacobsen, Bert De Brabandere, Arnold W. M. Smeulders
2017	J	`jnl`	Multiscale Hierarchical Convolutional Networks. CoRR Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders
2016	A*	`conf`	Structured Receptive Fields in CNNs. CVPR Jörn-Henrik Jacobsen, Jan C. van Gemert, Zhongyu Lou, Arnold W. M. Smeulders
2016	J	`jnl`	Structured Receptive Fields in CNNs. CoRR Jörn-Henrik Jacobsen, Jan C. van Gemert, Zhongyu Lou, Arnold W. M. Smeulders