Researchers have published a note on non-negative $L_1$-approximating polynomials with respect to Gaussian distributions. This work establishes that sets with a bounded Gaussian surface area admit degree-$k$ non-negative polynomials that $\epsilon$-approximate their indicator functions in $L_1$-norm. The findings match existing degree bounds for Gaussian $L_1$-approximation but add the constraint of non-negativity. AI
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IMPACT This research contributes to the theoretical underpinnings of computational learning theory, potentially influencing future developments in smoothed learning from positive-only examples.
RANK_REASON The cluster contains an academic paper published on arXiv.