Approximate algorithm for categorical structured inference with noisy observations achieves Hamming error logarithmic in the number of categories, generalizing prior binary-label results.
For every k ≥ 3, there is a polynomial time factor 0.7666 approximation algorithm for MaxAgree[k] on general graphs
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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.