no code implementations • 19 Jun 2024 • Julius von Kügelgen

Causal representation learning (CRL) aims to combine the core strengths of ML and causality by learning representations in the form of latent variables endowed with causal model semantics.

1 code implementation • 13 Mar 2024 • Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane

Causal representation learning aims at identifying high-level causal variables from perceptual data.

no code implementations • 20 Dec 2023 • Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

As typical in ICA, previous work focused on the case with an equal number of latent components and observed mixtures.

no code implementations • 15 Nov 2023 • Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim

Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data.

1 code implementation • 7 Nov 2023 • Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello

We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities.

no code implementations • 11 Oct 2023 • Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights.

no code implementations • 19 Jul 2023 • Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf

To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains.

1 code implementation • 9 Jun 2023 • Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments.

1 code implementation • NeurIPS 2023 • Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David M. Blei, Bernhard Schölkopf

We study the fundamental setting of two causal variables and prove that the observational distribution and one perfect intervention per node suffice for identifiability, subject to a genericity condition.

1 code implementation • NeurIPS 2023 • Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf

As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results.

no code implementations • 23 May 2023 • Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel

Under this generative process, we prove that the ground-truth object representations can be identified by an invertible and compositional inference model, even in the presence of dependencies between objects.

1 code implementation • 14 Dec 2022 • Armin Kekić, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.

no code implementations • 1 Nov 2022 • Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared.

1 code implementation • 1 Oct 2022 • Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation.

2 code implementations • 20 Jul 2022 • Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.

1 code implementation • 6 Jun 2022 • Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve

Leveraging self-consistency, we show that the ELBO converges to a regularized log-likelihood.

1 code implementation • 4 Jun 2022 • Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen

In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.

no code implementations • 1 Apr 2022 • Bernhard Schölkopf, Julius von Kügelgen

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality.

no code implementations • 14 Feb 2022 • Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf

Model identifiability is a desirable property in the context of unsupervised representation learning.

1 code implementation • 2 Feb 2022 • Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing

We introduce an approach to counterfactual inference based on merging information from multiple datasets.

1 code implementation • 13 Oct 2021 • Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf

Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.

no code implementations • ICLR 2022 • Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.

1 code implementation • EMNLP 2021 • Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf

The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.

1 code implementation • ICLR 2022 • Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.

no code implementations • NeurIPS 2021 • Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter Gehler

; and (ii) if the new predictions differ from the current ones, should we update?

no code implementations • 22 Jun 2021 • Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf

Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.

1 code implementation • NeurIPS 2021 • Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve

Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process.

1 code implementation • NeurIPS 2021 • Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant.

Ranked #1 on Image Classification on Causal3DIdent

1 code implementation • 13 Oct 2020 • Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf

Algorithmic fairness is typically studied from the perspective of predictions.

1 code implementation • NeurIPS 2020 • Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.

1 code implementation • 14 May 2020 • Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf

We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.

Applications Methodology

no code implementations • 27 Apr 2020 • Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf

Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world.

no code implementations • 9 Oct 2019 • Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller

We study the problem of causal discovery through targeted interventions.

1 code implementation • 28 May 2019 • Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf

While the success of semi-supervised learning (SSL) is still not fully understood, Sch\"olkopf et al. (2012) have established a link to the principle of independent causal mechanisms.

1 code implementation • 20 Jul 2018 • Julius von Kügelgen, Alexander Mey, Marco Loog

Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only.

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