An algorithm learns high-dimensional Gaussians under unknown halfspace truncation with optimal Õ(d²/ε²) sample complexity via reinterpretation of low-degree moments through a relative truncation parameter.
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Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity
An algorithm learns high-dimensional Gaussians under unknown halfspace truncation with optimal Õ(d²/ε²) sample complexity via reinterpretation of low-degree moments through a relative truncation parameter.