Learning Structured Distributions

By Ilias Diakonikolas. 2016

69 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, methodology): “We refer the reader to surveys [7], [15], [18], [43] for a comprehensive review of recent results on discrete distribution learning and testing.”
Distributed Simulation and Distributed Inference (2018, background): “With more than a century of history (see the books [Tsy09, DL01]), distribution learning has recently seen a surge of interest in the computer science community as well, with a focus on discrete domains (see e.g. [ Dia16] for a survey of these recent developments).”
Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures (2016, unknown): “, [64], [28] and references therein).”
Testing Shape Restrictions of Discrete Distributions (2015, background): “Here, it is guaranteed or thought that the unknown distribution satisfies a shape constraint, such as having a monotone or log-concave probability density function [7, 25, 46, 54].”
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees (2018, background): “readers to the survey [19] for distribution estimation techniques.”
Communication-Efficient Distributed Learning of Discrete Probability Distributions (2017, background): “, [19]) that Θ(n/ε(2)) samples are necessary and sufficient to learn an unstructured distribution supported on n elements within `1-error ε.”
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain (2018, methodology): “…basic approach of using the empirical distribution (see e.g. Chan et al. (2013); Daskalakis et al. (2012); Kamath et al. (2015); Diakonikolas et al. (2014); Daskalakis et al. (2015); Diakonikolas (2016); Diakonikolas and Kane (2016); Diakonikolas et al. (2017b,a); Valiant and Valiant (2014, 2016)).”
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes (2018, background): “The theoretical machine learning community also has a rich literature on distribution learning; the recent survey [9] considers learning structured distributions, and the survey [13] focuses on mixtures of Gaussians.”