← Authors

Julius von Kügelgen

24 papers · 2020–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🏃 Academic Marathon (5) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (38) 👑 Triple Crown 🏆 Grand Slam 🤝 Dynamic Duo (19) 🗃️ Keyword Collector (78) 📈 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

Bernhard Schölkopf (19) Francesco Locatello (7) Luigi Gresele (6) Wieland Brendel (5) Michel Besserve (5) Cian Eastwood (3) Armin Kekić (3) Frederik Träuble (3) Peter Vincent Gehler (3) Jack Brady (3)

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