Julius von Kügelgen

DeepAI

Featured Co-authors

Bernhard Schölkopf
237 publications
Andreas Krause
165 publications
Thomas Brox
99 publications
Adrian Weller
82 publications
Matthias Bethge
59 publications
Marco Loog
52 publications
Dominik Janzing
50 publications
Francesco Locatello
44 publications
Mrinmaya Sachan
37 publications
Georg Martius
36 publications
Chris Russell
34 publications

research

∙ 12/14/2022

Evaluating vaccine allocation strategies using simulation-assisted causal modelling

Early on during a pandemic, vaccine availability is limited, requiring p...

Armin Kekić, et al. ∙

research

∙ 11/01/2022

Backtracking Counterfactuals

Counterfactual reasoning – envisioning hypothetical scenarios, or possib...

Julius von Kügelgen, et al. ∙

research

∙ 10/01/2022

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

In representation learning, a common approach is to seek representations...

Cian Eastwood, et al. ∙

research

∙ 06/06/2022

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

Variational autoencoders (VAEs) are a popular framework for modeling com...

Patrik Reizinger, et al. ∙

research

∙ 06/04/2022

Active Bayesian Causal Inference

Causal discovery and causal reasoning are classically treated as separat...

Christian Toth, et al. ∙

research

∙ 06/04/2022

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Machine learning approaches commonly rely on the assumption of independe...

Ronan Perry, et al. ∙

research

∙ 04/01/2022

From Statistical to Causal Learning

We describe basic ideas underlying research to build and understand arti...

Bernhard Schölkopf, et al. ∙

research

∙ 02/14/2022

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

Model identifiability is a desirable property in the context of unsuperv...

Shubhangi Ghosh, et al. ∙

research

∙ 02/02/2022

Causal Inference Through the Structural Causal Marginal Problem

We introduce an approach to counterfactual inference based on merging in...

Luigi Gresele, et al. ∙

research

∙ 10/13/2021

Unsupervised Object Learning via Common Fate

Learning generative object models from unlabelled videos is a long stand...

Matthias Tangemann, et al. ∙

research

∙ 10/11/2021

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

Predicting the future trajectory of a moving agent can be easy when the ...

Osama Makansi, et al. ∙

research

∙ 10/07/2021

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

The principle of independent causal mechanisms (ICM) states that generat...

Zhijing Jin, et al. ∙

research

∙ 07/17/2021

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

An important component for generalization in machine learning is to unco...

Lukas Schott, et al. ∙

research

∙ 07/02/2021

Backward-Compatible Prediction Updates: A Probabilistic Approach

When machine learning systems meet real world applications, accuracy is ...

Frederik Träuble, et al. ∙

research

∙ 06/22/2021

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

Algorithmic recourse aims to provide actionable recommendations to indiv...

Julius von Kügelgen, et al. ∙

research

∙ 06/09/2021

Independent mechanism analysis, a new concept?

Independent component analysis provides a principled framework for unsup...

Luigi Gresele, et al. ∙

research

∙ 06/08/2021

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

Self-supervised representation learning has shown remarkable success in ...

Julius von Kügelgen, et al. ∙

research

∙ 10/13/2020

On the Fairness of Causal Algorithmic Recourse

While many recent works have studied the problem of algorithmic fairness...

Julius von Kügelgen, et al. ∙

research

∙ 10/01/2020

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

Two-sample and independence tests with the kernel-based MMD and HSIC hav...

Felix Laumann, et al. ∙

research

∙ 06/11/2020

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Recent work has discussed the limitations of counterfactual explanations...

Amir-Hossein Karimi, et al. ∙

research

∙ 05/14/2020

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

We point out an example of Simpson's paradox in COVID-19 case fatality r...

Julius von Kügelgen, et al. ∙

research

∙ 04/27/2020

Towards causal generative scene models via competition of experts

Learning how to model complex scenes in a modular way with recombinable ...

Julius von Kügelgen, et al. ∙

research

∙ 04/16/2020

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

The United Nations' ambitions to combat climate change and prosper human...

Felix Laumann, et al. ∙

research

∙ 10/09/2019

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

We study the problem of causal discovery through targeted interventions....

Julius von Kügelgen, et al. ∙

research

∙ 05/28/2019

Semi-Supervised Learning, Causality and the Conditional Cluster Assumption

While the success of semi-supervised learning (SSL) is still not fully u...

Julius von Kügelgen, et al. ∙

research

∙ 07/20/2018

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

This paper presents a novel, causally-inspired approach to domain adapta...

Julius von Kügelgen, et al. ∙