Julius von Kügelgen
24 papers · 2020–2025 · 7 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (36) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
👑
Triple Crown
🏆
Grand Slam
🤝
Dynamic Duo
(19)
🔬
Deep Specialist
(11)
🧭
Keyword Pioneer
💎
Century Club · Bronze
❓
The Questioner
🔥
Unstoppable · Bronze
⚡
Prolific Year · Productive
🗃️
Keyword Collector
(99)
Top co-authors
Research topics
Machine Learning
(21)
Artificial Intelligence
(17)
Core AI
(13)
Learning Types · Machine Learning
(11)
Core Methods
(8)
Optimization & Theory
(7)
Deep Learning
(6)
Knowledge & Reasoning
(4)
Application Areas
(4)
Bayesian & Probabilistic
(4)
Models
(4)
Reasoning
(4)
Learning Types · Deep Learning
(3)
Learning Paradigms
(2)
Computer Vision
(1)
Keywords
causal inference
(6)
representation learning
(5)
domain generalization
(3)
independent component analysis
(3)
interventional data
(2)
causal graph
(2)
generative model
(2)
counterfactual reasoning
(2)
causal representation learning
(2)
structural causal model
(2)
unsupervised learning
(2)
disentangled representation
(2)
causal discovery
(2)
algorithmic recourse
(2)
distribution shift
(2)
variational autoencoder
(2)
latent factor
(2)
natural language processing
(1)
bayesian inference
(1)
image generation
(1)
Papers
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
ICLR 2025
A Sparsity Principle for Partially Observable Causal Representation Learning
ICML 2024
Multi-View Causal Representation Learning with Partial Observability
ICLR 2024
Backtracking Counterfactuals
CLeaR 2023
Causal Component Analysis
NeurIPS 2023
Causal effect estimation from observational and interventional data through matrix weighted linear estimators
UAI 2023
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
ICLR 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
NeurIPS 2023
Provably Learning Object-Centric Representations
ICML 2023
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
NeurIPS 2023
Unsupervised Object Learning via Common Fate
CLeaR 2023
Active Bayesian Causal Inference
NeurIPS 2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
NeurIPS 2022
Causal Inference Through the Structural Causal Marginal Problem
ICML 2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
NeurIPS 2022
On the Fairness of Causal Algorithmic Recourse
AAAI 2022
Probable Domain Generalization via Quantile Risk Minimization
NeurIPS 2022
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
ICLR 2022
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
ICLR 2022
Backward-Compatible Prediction Updates: A Probabilistic Approach
NeurIPS 2021
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
EMNLP 2021
Independent mechanism analysis, a new concept?
NeurIPS 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
NeurIPS 2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
NeurIPS 2020