Julius von Kügelgen

Orcid: 0000-0001-6469-4118

According to our database¹, Julius von Kügelgen authored at least 44 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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In proceedings

Article

PhD thesis

Dataset

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On csauthors.net:

Bibliography

2024

Deep Backtracking Counterfactuals for Causally Compliant Explanations.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2024

Interaction Asymmetry: A General Principle for Learning Composable Abstractions.

[BibT_eX]

[DOI]

CoRR, 2024

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment.

[BibT_eX]

[DOI]

Julius von Kügelgen

CoRR, 2024

A Sparsity Principle for Partially Observable Causal Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-View Causal Representation Learning with Partial Observability.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

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

[BibT_eX]

[DOI]

Entropy, December, 2023

Evaluating vaccine allocation strategies using simulation-assisted causal modeling.

[BibT_eX]

[DOI]

Patterns, June, 2023

Independent Mechanism Analysis and the Manifold Hypothesis.

[BibT_eX]

[DOI]

CoRR, 2023

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Causal Component Analysis.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

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

[BibT_eX]

[DOI]

Cian Eastwood

Andrei Liviu Nicolicioiu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unsupervised Object Learning via Common Fate.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Backtracking Counterfactuals.

[BibT_eX]

[DOI]

Julius von Kügelgen

Abdirisak Mohamed

Sander Beckers

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

ima-vae.

[BibT_eX]

[DOI]

Dataset, June, 2022

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

[BibT_eX]

[DOI]

Julius von Kügelgen

Luigi Gresele

Bernhard Schölkopf

Dataset, May, 2022

Evaluating vaccine allocation strategies using simulation-assisted causal modelling.

[BibT_eX]

[DOI]

CoRR, 2022

From Statistical to Causal Learning.

[BibT_eX]

[DOI]

Bernhard Schölkopf

Julius von Kügelgen

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Active Bayesian Causal Inference.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

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

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the Fairness of Causal Algorithmic Recourse.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Julius von Kügelgen

Luigi Gresele

Bernhard Schölkopf

IEEE Trans. Artif. Intell., 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Backward-Compatible Prediction Updates: A Probabilistic Approach.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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?

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2020

Towards causal generative scene models via competition of experts.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

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

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the xxAI - Beyond Explainable AI, 2020

2019

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

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Julius von Kügelgen

Alexander Mey

Marco Loog

CoRR, 2018

Julius von Kügelgen

Timeline

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On csauthors.net:

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