A Method for Animating Children’s Drawings of the Human Figure
Harrison Jesse Smith, Qingyuan Zheng, Yifei Li, Somya Jain, Jessica K. Hodgins
Conference on Learning Theory (COLT)
We study the problem of robustly estimating the parameter pof an Erdős-Rényi random graph on n nodes, where a γ fraction of nodes may be adversarially corrupted. After showing the deficiencies of canonical estimators, we design a computationally-efficient spectral algorithm which estimates p up to accuracy Õ (√ p(1 − p) / n + γ √ p(1 − p) / √ n + γ/n) for γ < 1/60. Furthermore, we give an inefficient algorithm with similar accuracy for all γ < 1/2, the information-theoretic limit. Finally, we prove a nearly-matching statistical lower bound, showing that the error of our algorithms is optimal up to logarithmic factors.
Harrison Jesse Smith, Qingyuan Zheng, Yifei Li, Somya Jain, Jessica K. Hodgins
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