Julius von Kügelgen

71 papers A* 19A 3Journal 45Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations. CoRR Bohan Wu, Julius von Kügelgen, David M. Blei
2025	J	`jnl`	From Pixels to Components: Eigenvector Masking for Visual Representation Learning. CoRR Alice Bizeul, Thomas M. Sutter, Alain Ryser, Bernhard Schölkopf, Julius von Kügelgen, Julia E. Vogt
2025	A*	`conf`	Interaction Asymmetry: A General Principle for Learning Composable Abstractions. ICLR Jack Brady, Julius von Kügelgen, Sébastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel
2025	J	`jnl`	Representation Learning for Distributional Perturbation Extrapolation. CoRR Julius von Kügelgen, Jakob Ketterer, Xinwei Shen, Nicolai Meinshausen, Jonas Peters
2025	J	`jnl`	Transferring Causal Effects using Proxies. CoRR Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen, Jonas Peters
2024	A*	`conf`	A Sparsity Principle for Partially Observable Causal Representation Learning. ICML Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
2024	J	`jnl`	A Sparsity Principle for Partially Observable Causal Representation Learning. CoRR Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
2024	J	`jnl`	Deep Backtracking Counterfactuals for Causally Compliant Explanations. Trans. Mach. Learn. Res. Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
2024	J	`jnl`	Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment. CoRR Julius von Kügelgen
2024	J	`jnl`	Interaction Asymmetry: A General Principle for Learning Composable Abstractions. CoRR Jack Brady, Julius von Kügelgen, Sébastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel
2024	A*	`conf`	Multi-View Causal Representation Learning with Partial Observability. ICLR Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
2023	—	`conf`	Backtracking Counterfactuals. CLeaR Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers
2023	A*	`conf`	Causal Component Analysis. NeurIPS Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf
2023	J	`jnl`	Causal Component Analysis. CoRR Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf
2023	J	`jnl`	Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators. CoRR Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
2023	A	`conf`	Causal effect estimation from observational and interventional data through matrix weighted linear estimators. UAI Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
2023	A*	`conf`	DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. ICLR Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf
2023	J	`jnl`	Deep Backtracking Counterfactuals for Causally Compliant Explanations. CoRR Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach
2023	J	`jnl`	Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf
2023	J	`jnl`	Independent Mechanism Analysis and the Manifold Hypothesis. CoRR Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf
2023	J	`jnl`	Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. Entropy Felix Laumann, Julius von Kügelgen, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona
2023	J	`jnl`	Multi-View Causal Representation Learning with Partial Observability. CoRR Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
2023	A*	`conf`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	J	`jnl`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	A*	`conf`	Provably Learning Object-Centric Representations. ICML Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
2023	J	`jnl`	Provably Learning Object-Centric Representations. CoRR Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
2023	J	`jnl`	Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. CoRR Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim
2023	A*	`conf`	Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. NeurIPS Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica Pogancic, Julius von Kügelgen, Bernhard Schölkopf
2023	J	`jnl`	Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. CoRR Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf
2023	—	`conf`	Unsupervised Object Learning via Common Fate. CLeaR Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf
2022	A*	`conf`	Active Bayesian Causal Inference. NeurIPS Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen
2022	J	`jnl`	Active Bayesian Causal Inference. CoRR Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen
2022	J	`jnl`	Backtracking Counterfactuals. CoRR Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers
2022	A*	`conf`	Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. NeurIPS Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf
2022	J	`jnl`	Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. CoRR Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf
2022	A*	`conf`	Causal Inference Through the Structural Causal Marginal Problem. ICML Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
2022	J	`jnl`	Causal Inference Through the Structural Causal Marginal Problem. CoRR Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
2022	J	`jnl`	DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. CoRR Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf
2022	A*	`conf`	Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
2022	J	`jnl`	Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
2022	J	`jnl`	Evaluating vaccine allocation strategies using simulation-assisted causal modelling. CoRR Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf
2022	J	`jnl`	From Statistical to Causal Learning. CoRR Bernhard Schölkopf, Julius von Kügelgen
2022	J	`jnl`	On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". CoRR Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf
2022	A*	`conf`	On the Fairness of Causal Algorithmic Recourse. AAAI Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf
2022	A*	`conf`	Probable Domain Generalization via Quantile Risk Minimization. NeurIPS Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf
2022	J	`jnl`	Probable Domain Generalization via Quantile Risk Minimization. CoRR Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf
2022	A*	`conf`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2022	A*	`conf`	You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
2021	J	`jnl`	Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. CoRR Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf
2021	A*	`conf`	Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
2021	J	`jnl`	Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler
2021	—	`conf`	Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. EMNLP (1) Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf
2021	J	`jnl`	Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. CoRR Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf
2021	A*	`conf`	Independent mechanism analysis, a new concept? NeurIPS Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve
2021	J	`jnl`	Independent mechanism analysis, a new concept? CoRR Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve
2021	A*	`conf`	Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
2021	J	`jnl`	Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
2021	J	`jnl`	Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects. IEEE Trans. Artif. Intell. Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
2021	J	`jnl`	Unsupervised Object Learning via Common Fate. CoRR Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf
2021	J	`jnl`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2021	J	`jnl`	You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf
2020	J	`jnl`	A bacterial size law revealed by a coarse-grained model of cell physiology. PLoS Comput. Biol. François Bertaux, Julius von Kügelgen, Samuel Marguerat, Vahid Shahrezaei
2020	J	`jnl`	Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. CoRR Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera
2020	J	`jnl`	On the Fairness of Causal Algorithmic Recourse. CoRR Julius von Kügelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, Bernhard Schölkopf
2020	A	`conf`	Semi-supervised learning, causality, and the conditional cluster assumption. UAI Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf
2020	—	`conf`	Towards Causal Algorithmic Recourse. xxAI@ICML Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera
2020	J	`jnl`	Towards causal generative scene models via competition of experts. CoRR Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf
2019	J	`jnl`	Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. CoRR Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller
2019	A	`conf`	Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS Julius von Kügelgen, Alexander Mey, Marco Loog
2019	J	`jnl`	Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf
2018	J	`jnl`	Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR Julius von Kügelgen, Alexander Mey, Marco Loog