Julius von Kügelgen

Exploring the intersection of causal inference and machine learning

Summary

MPI for Intelligent Systems / W. Scheible

I am a PhD student with Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems in Tübingen. As part of the Cambridge-Tübingen programme, I am also co-supervised by Adrian Weller at the University of Cambridge, where I spent the first year of my PhD.

My research interests lie at the intersection of causal inference and machine learning, see Research Interests for more details.

Previously, I studied Mathematics (BSc+MSci) at Imperial College London and Artificial Intelligence (MSc) at UPC Barcelona in Spain and at TU Delft in the Netherlands.

I am originally from the beautiful Hamburg in northern Germany.

News

Selected publications & preprints

Probable Domain Generalization via Quantile Risk Minimization.

Cian Eastwood*, Alexander Robey*, Shashank Singh, JvK, Hamed Hassani, George J. Pappas, Bernhard Schölkopf.

[arXiv]

Active Bayesian Causal Inference.

Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz*, JvK*. (*shared last author)

[arXiv]

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

Ronan Perry, JvK*, Bernhard Schölkopf*. (*shared last author)

[arXiv]

From Statistical to Causal Learning.

Bernhard Schölkopf*, JvK*. (*equal contribution)

Proceedings of the International Congress of Mathematicians 2022 (to appear).

[arXiv]

Causal Inference Through the Structural Causal Marginal Problem.

Luigi Gresele*, JvK*, Jonas M. Kübler*, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing. (*equal contribution)

ICML 2022

[arXiv]

On the Fairness of Causal Algorithmic Recourse.

JvK, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf.

AAAI 2022 (oral)
(Previously: ICML 2021 Workshop Algorithmic Recourse; NeurIPS 2020 Workshop Algorithmic Fairness through the Lens of Causality and Interpretability (AFCI) )

[paper] [code] [poster] [video]

Self-supervised learning with data augmentations provably isolates content from style.

JvK*, Yash Sharma*, Luigi Gresele*, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello. (*equal contribution)

NeurIPS 2021

[arXiv] [code]

Independent mechanism analysis, a new concept?

Luigi Gresele*, JvK*, Vincent Stimper, Bernhard Schölkopf, Michel Besserve. (*equal contribution)

NeurIPS 2021

[arXiv] [code]

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

Zhijing Jin*, JvK*, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf. (*equal contribution)

EMNLP 2021 (oral)

[arXiv] [code] [poster] [slides] [talk]

Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects.

JvK*, Luigi Gresele*, Bernhard Schölkopf. (*equal contribution)

IEEE Transactions on Artificial Intelligence, 2021.

[paper] [data & code] [video1] [video2]

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Amir-Hossein Karimi*, JvK*, Bernhard Schölkopf, Isabel Valera. (*equal contribution)

NeurIPS 2020 (spotlight; top 4% of submissions)
(Previously: ICML 2020 Workshops

  1. XXAI: Extending Explainable AI Beyond Deep Models and Classifiers (oral; 4 out of 20 papers)

  2. WHI: Workshop on Human Interpretability in Machine Learning (oral; 4 out of 50 papers))

[paper] [short presentation] [long presentation (@UCL reading group)] [poster]

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

JvK, Alexander Mey, Marco Loog, Bernhard Schölkopf.

UAI 2020
(Previously: NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making)

[paper] [poster]

Semi-generative modelling: covariate-shift adaptation with cause and effect features.

JvK, Alexander Mey, Marco Loog.

AISTATS 2019
(Previously: ICML 2018 Workshop CausalML)

[paper]

Other publications & preprints

On the DCI framework for evaluating disentangled representations: Extensions and connections to identifiability

Cian Eastwood*, Andrei Liviu Nicolicioiu*, JvK*, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf. (*equal contribution)

1st Workshop on Causal Representation Learning @ UAI 2022

Embrace the Gap: VAEs Perform Independent Mechanism Analysis.

Patrik Reizinger, Luigi Gresele, Jack Brady, JvK, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve.

5th Workshop on Tractable Probabilistic Modeling @ UAI 2022

[arXiv]

Complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change: a network analysis

Felix Laumann, JvK, Thiago Hector Kanashiro Uehara, Mauricio Barahona

The Lancet Planetary Health

[OpenAccess]

Towards Causal Algorithmic Recourse.

Amir-Hossein Karimi*, JvK*, Bernhard Schölkopf, Isabel Valera.

xxAI - Beyond Explainable AI. Lecture Notes in Computer Science, vol 13200. Springer (2022).

[OpenAccess]

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

Osama Makansi, JvK, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf.

ICLR 2022

[arXiv]

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

Lukas Schott, JvK, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel.

ICLR 2022
(Previously at: ICLR 2021 Workshop Generalization beyond the training distribution in brains and machines )

[paper] [poster] [code/benchmark]

Backward-Compatible Prediction Updates: A Probabilistic Approach.

Frederik Träuble, JvK, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter Gehler.

NeurIPS 2021

[
arXiv]

Unsupervised Object Learning via Common Fate.

Matthias Tangemann, Steffen Schneider, JvK, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf.

[arXiv]

Algorithmic recourse in partially and fully confounded settings through bounding counterfactual effects.

JvK, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf.

ICML 2021 Workshop Algorithmic Recourse

[
arXiv]

Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes.

Felix Laumann, JvK, Mauricio Barahona.

7th International Conference on Time Series and Forecasting (ITISE 2021)

[paper]

Towards causal generative scene models via competition of experts.

JvK*, Ivan Ustyuzhaninov*, Peter Gehler, Matthias Bethge, Bernhard Schölkopf. (*equal contribution)

ICLR 2020 Workshop Causal Learning for Decision Making (CLDM)

[paper] [presentation (video)]

Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals.

Felix Laumann, JvK, Mauricio Barahona.

ICLR 2020 Workshop Tackling Climate Change with Machine Learning (CCAI)

[paper] [slides]

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

JvK, Paul K Rubenstein, Bernhard Schölkopf, Adrian Weller.

NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making

[paper] [poster]