AI RESEARCH
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
arXiv CS.LG
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ArXi:2410.01656v2 Announce Type: replace-cross We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set $S \subseteq \mathbb{R}^d$. Kontonis, Tzamos, and Zampetakis (FOCS'19) gave a $d^{\mathrm{poly}(1/\varepsilon)}$ time algorithm for finding $\varepsilon$-accurate parameters for the special case of Gaussian distributions with diagonal covariance matrix.