Local resampling and backtracking algorithms for the Lovász Local Lemma achieve near-linear total work in the number of adaptive updates when constraints are added or removed.
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Approximate algorithm for categorical structured inference with noisy observations achieves Hamming error logarithmic in the number of categories, generalizing prior binary-label results.
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Dynamic Construction of the Lov\'asz Local Lemma
Local resampling and backtracking algorithms for the Lovász Local Lemma achieve near-linear total work in the number of adaptive updates when constraints are added or removed.
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Approximate Inference in Structured Instances with Noisy Categorical Observations
Approximate algorithm for categorical structured inference with noisy observations achieves Hamming error logarithmic in the number of categories, generalizing prior binary-label results.