Julius von Kügelgen
24 papers · 2020–2025 · 7 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (5) 🐝 Cross-Pollinator (13) 🌍 Conference Polyglot (7) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5)
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Renaissance Researcher
(5)
🌉
Interdisciplinary Bridge
🗺️
Taxonomy Completionist
(38)
👑
Triple Crown
🏆
Grand Slam
🤝
Dynamic Duo
(19)
🗃️
Keyword Collector
(78)
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Trend Setter
💎
Century Club
(24)
⚡
Prolific Year
(8)
🔥
Unstoppable
(6)
❓
The Questioner
Conferences
NIPS (11)
ICLR (5)
ICML (3)
CLEAR (2)
AAAI (1)
EMNLP (1)
UAI (1)
Top co-authors
Keywords
causal inference
(5)
representation learning
(3)
counterfactual reasoning
(2)
algorithmic recourse
(2)
independent component analysis
(2)
domain generalization
(2)
structural causal model
(2)
distribution shift
(2)
interventional datum
(2)
causal graph
(2)
generative model
(2)
unsupervised learning
(2)
causal representation learning
(2)
causal discovery
(2)
natural language processing
(1)
probabilistic modeling
(1)
bayesian inference
(1)
image generation
(1)
causal reasoning
(1)
gaussian process
(1)
Papers
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
ICLR 2025
Multi-View Causal Representation Learning with Partial Observability
ICLR 2024
A Sparsity Principle for Partially Observable Causal Representation Learning
ICML 2024
Causal effect estimation from observational and interventional data through matrix weighted linear estimators
UAI 2023
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
NIPS 2023
Causal Component Analysis
NIPS 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
NIPS 2023
Backtracking Counterfactuals
CLEAR 2023
Unsupervised Object Learning via Common Fate
CLEAR 2023
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
ICLR 2023
Provably Learning Object-Centric Representations
ICML 2023
On the Fairness of Causal Algorithmic Recourse
AAAI 2022
Causal Inference Through the Structural Causal Marginal Problem
ICML 2022
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
ICLR 2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
NIPS 2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
NIPS 2022
Active Bayesian Causal Inference
NIPS 2022
Probable Domain Generalization via Quantile Risk Minimization
NIPS 2022
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
ICLR 2022
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
EMNLP 2021
Backward-Compatible Prediction Updates: A Probabilistic Approach
NIPS 2021
Independent mechanism analysis, a new concept?
NIPS 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
NIPS 2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
NIPS 2020