Patrik Reizinger
Scholar

Patrik Reizinger

Google Scholar ID: zIT0fdIAAAAJ
Max Planck Institute for Intelligent Systems
Causal Representation LearningIndependent Component AnalysisMachine LearningCausal Inference
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Citations & Impact
All-time
Citations
191
 
H-index
7
 
i10-index
7
 
Publications
20
 
Co-authors
40
list available
Contact
No contact links provided.
Publications
11 items
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
2026
Cited
0
Causality is Key for Interpretability Claims to Generalise
2026
Cited
0
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
2025
Cited
0
Out-of-distribution Tests Reveal Compositionality in Chess Transformers
2025
Cited
0
Estimating Treatment Effects with Independent Component Analysis
2025
Cited
0
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
2025
Cited
0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
2025
Cited
0
From superposition to sparse codes: interpretable representations in neural networks
2025
Cited
0
Resume (English only)
Co-authors
40 total
Wieland Brendel
Wieland Brendel
Fellow at ELLIS Institut Tübingen, Group Leader, Max Planck Institute for Intelligent Systems
Ferenc Huszár
Ferenc Huszár
University of Cambridge
Bernhard Schölkopf
Bernhard Schölkopf
Director, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH
Co-author 4
Co-author 4
Szilvia Ujváry
Szilvia Ujváry
PhD candidate, University of Cambridge
David Klindt
David Klindt
Cold Spring Harbor Laboratory
Randall Balestriero
Randall Balestriero
AI Researcher
Co-author 8
Co-author 8