SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.
Biostatistics , volume =
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Inferring data distributions precisely allows distilling exact unlearning signals, yielding KL divergence bounds to the retrained model and outperforming competitors in three forgetting scenarios.
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Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.
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Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning
Inferring data distributions precisely allows distilling exact unlearning signals, yielding KL divergence bounds to the retrained model and outperforming competitors in three forgetting scenarios.