A sheaf-theoretic framework for causal abstraction networks that represents and learns consistent collections of mixture causal models across distributed agents.
A splitting method for orthogonality constrained problems,
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Networks of Causal Abstractions: A Sheaf-theoretic Framework
A sheaf-theoretic framework for causal abstraction networks that represents and learns consistent collections of mixture causal models across distributed agents.