Call for Papers

We welcome submissions related to any aspects of CRL, including but not limited to:

• Learning latent (structural) causal models & structured (deep) generative models
• Interventional representations, causal digital twins & structured (causal) world models
• Post-hoc extraction of causal relations from (deep) generative models
• Self-supervised causal representation learning
• Multi-environment & multi-view causal representation learning
• Micro vs. macro/coarse-grained/multi-level causal systems
• Identifiable representation learning & nonlinear ICA
• Uncertainty quantification in (causal) representation learning
• Group-theoretic & symmetry-based views on disentanglement
• Invariance & equivariance in representation learning
• Interdisciplinary perspectives on causal representation learning, including from cognitive science, psychology, (computational) neuroscience or philosophy
• Real-world applications of causal representation learning, including in biology, medical sciences, or robotics

Submissions should present novel, unpublished work. Work that previously appeared in non-archival venues (such as arXiv or other workshops without proceedings) is allowed.

The CRL workshop is non-archival, and should thus generally not violate dual submission policies at other archival venues (e.g., submitting work that is currently under review at another conference such as NeurIPS is permitted); if unsure, please check yourself with the corresponding venue.