Estimating the Unseen

By Paul Valiant, Gregory Valiant. 2013

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

On Distributed Differential Privacy and Counting Distinct Elements (2020, methodology): “To prove Theorem 2, we build on the moment matching method from the literature on (non-private) distribution estimation, namely [43, 45], and tailor it to CountDistinct in the DP local setting (see Section 3.1 for more details on this connection).”
Chebyshev polynomials, moment matching, and optimal estimation of the unseen (2015, methodology): “In this large-scale experiment, the original MATLAB code of the linear programming estimator given in [41] is extremely slow; the result in Fig.”
Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation (2014, methodology): “In the “data-starved” regime of n = o ( k 2 log 2 k ), empirical entropy is strictly rate-suboptimal.”
Maximum Likelihood Estimation of Functionals of Discrete Distributions (2014, background): “It was Valiant and Valiant [16] who first explicitly constructed a linear programming based estimator (later modified in [17]) that achieves consistency in entropy estimation with n≫ S/ lnS samples, which they also proved to be necessary.”
Learning Populations of Parameters (2017, background): “ed in this paper is the work on recovering an approximation to the unlabeled set of probabilities of domain elements, given independent draws from a 3 distribution of large discrete support (see e.g. [11, 2, 15, 16, 1]). Instead of learning the distribution, these works considered the alternate goal”
Minimax Estimation of Functionals of Discrete Distributions (2014, methodology): “Valiant and Valiant [9] observed that the MLE performs well when enough samples are available for entropy estimation.”
INSPECTRE: Privately Estimating the Unseen (2018, background): “, 2017b; Obremski and Skorski, 2017; Wu and Yang, 2019), support coverage and support size estimation (Valiant and Valiant, 2017, 2016; Raghunathan et al., 2017; Orlitsky et al., 2016; Wu and Yang, 2019).”
Minimax Estimation of the $L_1$ Distance (2017, methodology): “We would like to thank Gregory Valiant for discussing the estimator in [5].”