Julius von Kügelgen

According to our database, Julius von Kügelgen authored at least 42 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Sparsity Principle for Partially Observable Causal Representation Learning.

Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane

CoRR, 2024

2023

Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.

Felix Laumann, Julius von Kügelgen, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona

Entropy, December, 2023

Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

Patterns, June, 2023

Independent Mechanism Analysis and the Manifold Hypothesis.

Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

CoRR, 2023

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations.

Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim

CoRR, 2023

Multi-View Causal Representation Learning with Partial Observability.

Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello

CoRR, 2023

Deep Backtracking Counterfactuals for Causally Compliant Explanations.

Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

CoRR, 2023

Causal effect estimation from observational and interventional data through matrix weighted linear estimators.

Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Causal Component Analysis.

Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Nonparametric Identifiability of Causal Representations from Unknown Interventions.

Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features.

Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica Pogancic, Julius von Kügelgen, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Provably Learning Object-Centric Representations.

Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel

Proceedings of the International Conference on Machine Learning, 2023

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability.

Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unsupervised Object Learning via Common Fate.

Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Backtracking Counterfactuals.

Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

ima-vae.

Patrik Reizinger, Luigi Gresele, Jack Brady, Dominik Zietlow, Julius von Kügelgen, Michel Besserve, Georg Martius, Wieland Brendel, Bernhard Schölkopf

Dataset, June, 2022

Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal.

Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf

Dataset, May, 2022

Evaluating vaccine allocation strategies using simulation-assisted causal modelling.

Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

CoRR, 2022

From Statistical to Causal Learning.

Bernhard Schölkopf, Julius von Kügelgen

CoRR, 2022

On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective".

Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

CoRR, 2022

Active Bayesian Causal Inference.

Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Embrace the Gap: VAEs Perform Independent Mechanism Analysis.

Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis.

Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Probable Domain Generalization via Quantile Risk Minimization.

Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Causal Inference Through the Structural Causal Marginal Problem.

Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing

Proceedings of the International Conference on Machine Learning, 2022

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.

Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

Proceedings of the Tenth International Conference on Learning Representations, 2022

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.

Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the Fairness of Causal Algorithmic Recourse.

Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects.

Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf

IEEE Trans. Artif. Intell., 2021

Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects.

Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf

CoRR, 2021

Backward-Compatible Prediction Updates: A Probabilistic Approach.

Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.

Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Independent mechanism analysis, a new concept?

Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP.

Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

A bacterial size law revealed by a coarse-grained model of cell physiology.

François Bertaux, Julius von Kügelgen, Samuel Marguerat, Vahid Shahrezaei

PLoS Comput. Biol., 2020

Towards causal generative scene models via competition of experts.

Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf

CoRR, 2020

Semi-supervised learning, causality, and the conditional cluster assumption.

Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach.

Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Towards Causal Algorithmic Recourse.

Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks.

Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller

CoRR, 2019

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features.

Julius von Kügelgen, Alexander Mey, Marco Loog

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features.

Julius von Kügelgen, Alexander Mey, Marco Loog

CoRR, 2018