A near-optimal recovery algorithm for noisy k-XOR achieves the information-theoretic sample scaling with optimal noise dependence and is matched by low-degree lower bounds.
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Near Optimal Algorithms for Noisy $k$-XOR under Low-Degree Heuristic
A near-optimal recovery algorithm for noisy k-XOR achieves the information-theoretic sample scaling with optimal noise dependence and is matched by low-degree lower bounds.