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2020 – today
- 2024
[j5]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations. Trans. Mach. Learn. Res. 2024 (2024)- [c26]Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello:
Multi-View Causal Representation Learning with Partial Observability. ICLR 2024 - [c25]Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane:
A Sparsity Principle for Partially Observable Causal Representation Learning. ICML 2024 - [i35]Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane:
A Sparsity Principle for Partially Observable Causal Representation Learning. CoRR abs/2403.08335 (2024) - [i34]Julius von Kügelgen:
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment. CoRR abs/2406.13371 (2024) - 2023
- [j4]Felix Laumann, Julius von Kügelgen
, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona:
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. Entropy 25(12): 1597 (2023) - [j3]Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf:
Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns 4(6): 100739 (2023) - [c24]Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers:
Backtracking Counterfactuals. CLeaR 2023: 177-196 - [c23]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c22]Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf:
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. ICLR 2023 - [c21]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. ICML 2023: 3038-3062 - [c20]Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica Pogancic, Julius von Kügelgen, Bernhard Schölkopf:
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. NeurIPS 2023 - [c19]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS 2023 - [c18]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. NeurIPS 2023 - [c17]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal effect estimation from observational and interventional data through matrix weighted linear estimators. UAI 2023: 1087-1097 - [i33]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. CoRR abs/2305.14229 (2023) - [i32]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. CoRR abs/2305.17225 (2023) - [i31]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i30]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators. CoRR abs/2306.06002 (2023) - [i29]Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf:
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. CoRR abs/2307.09933 (2023) - [i28]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations. CoRR abs/2310.07665 (2023) - [i27]Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello:
Multi-View Causal Representation Learning with Partial Observability. CoRR abs/2311.04056 (2023) - [i26]Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim:
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. CoRR abs/2311.08815 (2023) - [i25]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
Independent Mechanism Analysis and the Manifold Hypothesis. CoRR abs/2312.13438 (2023) - 2022
- [c16]Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. AAAI 2022: 9584-9594 - [c15]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR 2022 - [c14]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 - [c13]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. ICML 2022: 7793-7824 - [c12]Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf:
Probable Domain Generalization via Quantile Risk Minimization. NeurIPS 2022 - [c11]Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf:
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. NeurIPS 2022 - [c10]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS 2022 - [c9]Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. NeurIPS 2022 - [d2]Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf:
Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal. IEEE DataPort, 2022
- [d1]Patrik Reizinger, Luigi Gresele, Jack Brady, Dominik Zietlow, Julius von Kügelgen, Michel Besserve, Georg Martius, Wieland Brendel, Bernhard Schölkopf:
ima-vae. Zenodo, 2022 - [i24]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. CoRR abs/2202.01300 (2022) - [i23]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". CoRR abs/2202.06844 (2022) - [i22]Bernhard Schölkopf, Julius von Kügelgen:
From Statistical to Causal Learning. CoRR abs/2204.00607 (2022) - [i21]Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf:
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. CoRR abs/2206.02013 (2022) - [i20]Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. CoRR abs/2206.02063 (2022) - [i19]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR abs/2206.02416 (2022) - [i18]Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf:
Probable Domain Generalization via Quantile Risk Minimization. CoRR abs/2207.09944 (2022) - [i17]Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf:
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. CoRR abs/2210.00364 (2022) - [i16]Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers:
Backtracking Counterfactuals. CoRR abs/2211.00472 (2022) - [i15]Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf:
Evaluating vaccine allocation strategies using simulation-assisted causal modelling. CoRR abs/2212.08498 (2022) - 2021
- [j2]Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf:
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects. IEEE Trans. Artif. Intell. 2(1): 18-27 (2021) - [c8]Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf:
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. EMNLP (1) 2021: 9499-9513 - [c7]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS 2021: 116-128 - [c6]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS 2021: 16451-16467 - [c5]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? NeurIPS 2021: 28233-28248 - [i14]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR abs/2106.04619 (2021) - [i13]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? CoRR abs/2106.05200 (2021) - [i12]Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf:
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. CoRR abs/2106.11849 (2021) - [i11]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR abs/2107.01057 (2021) - [i10]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR abs/2107.08221 (2021) - [i9]Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf:
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. CoRR abs/2110.03618 (2021) - [i8]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR abs/2110.05304 (2021) - [i7]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CoRR abs/2110.06562 (2021) - 2020
- [j1]François Bertaux, Julius von Kügelgen, Samuel Marguerat, Vahid Shahrezaei:
A bacterial size law revealed by a coarse-grained model of cell physiology. PLoS Comput. Biol. 16(9) (2020) - [c4]Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Towards Causal Algorithmic Recourse. xxAI@ICML 2020: 139-166 - [c3]Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. NeurIPS 2020 - [c2]Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf:
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020: 1-10 - [i6]Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf:
Towards causal generative scene models via competition of experts. CoRR abs/2004.12906 (2020) - [i5]Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. CoRR abs/2006.06831 (2020) - [i4]Julius von Kügelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. CoRR abs/2010.06529 (2020)
2010 – 2019
- 2019
- [c1]Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS 2019: 1361-1369 - [i3]Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf:
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR abs/1905.12081 (2019) - [i2]Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller:
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. CoRR abs/1910.03962 (2019) - 2018
- [i1]Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR abs/1807.07879 (2018)
Coauthor Index
[j5] [j4] [j3] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [i33] [i32] [i31] [i30] [i29] [i28] [i26] [i25] [c16] [c15] [c14] [c13] [c12] [c11] [c10] [d2] [d1] [i24] [i23] [i22] [i21] [i19] [i18] [i17] [i15] [j2] [c8] [c7] [c6] [c5] [i14] [i13] [i12] [i11] [i10] [i9] [i8] [i7] [c4] [c3] [c2] [i6] [i5] [i4] [i3] [i2]
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