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Optimal Prediction of the Number of Unseen Species with Multiplicity
Yi Hao · Ping Li

Wed Dec 09 08:20 PM -- 08:30 PM (PST) @ Orals & Spotlights: Learning Theory
in Orals & Spotlights Track 24: Learning Theory »
Based on a sample of size $n$, we consider estimating the number of symbols that appear at least $\mu$ times in an independent sample of size $a \cdot n$, where $a$ is a given parameter. This formulation includes, as a special case, the well-known problem of inferring the number of unseen species introduced by [Fisher et al.] in 1943 and considered by many others. Of considerable interest in this line of works is the largest $a$ for which the quantity can be accurately predicted. We completely resolve this problem by determining the limit of estimation to be $a \approx (\log n)/\mu$, with both lower and upper bounds matching up to constant factors. For the particular case of $\mu = 1$, this implies the recent result by [Orlitsky et al.] on the unseen species problem. Experimental evaluations show that the proposed estimator performs exceptionally well in practice. Furthermore, the estimator is a simple linear combination of symbols' empirical counts, and hence linear-time computable.

Author Information

Yi Hao (University of California, San Diego)

Fifth-year Ph.D. student supervised by Prof. Alon Orlitsky at UC San Diego. Broadly interested in Machine Learning, Learning Theory, Algorithm Design, Symbolic and Numerical Optimization. Seeking a summer 2020 internship in Data Science and Machine Learning.

Ping Li (Baidu Research USA)

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