59 citations · 252 across the 21 of their papers we have counts for
9 papers · 1 filter
Convergence Rates for Empirical Estimation of Binary Classification Bounds
Salimeh Yasaei Sekeh, Morteza Noshad, Kevin R. Moon +1
Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information th…
Ensemble Estimation of Distributional Functionals via -Nearest Neighbors
Kevin R. Moon, Kumar Sricharan, Alfred O. Hero
The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to t…
Direct Ensemble Estimation of Density Functionals
Alan Wisler, Kevin Moon, Visar Berisha
Estimating density functionals of analog sources is an important problem in statistical signal processing and information theory. Traditionally, estimating these quantities require…
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
Morteza Noshad, Kevin R. Moon, Salimeh Yasaei Sekeh +1
We propose a direct estimation method for Rényi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets and , re…
Ensemble Estimation of Generalized Mutual Information with Applications to Genomics
Kevin R. Moon, Kumar Sricharan, Alfred O. Hero
Mutual information is a measure of the dependence between random variables that has been used successfully in myriad applications in many fields. Generalized mutual information mea…
Information Theoretic Structure Learning with Confidence
Kevin R. Moon, Morteza Noshad, Salimeh Yasaei Sekeh +1
Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in hi…
Ensemble Estimation of Information Divergence
Kevin R. Moon, Kumar Sricharan, Kristjan Greenewald +1
Recent work has focused on the problem of nonparametric estimation of information divergence functionals. Many existing approaches are restrictive in their assumptions on the densi…
Multivariate f-Divergence Estimation With Confidence
Kevin R. Moon, Alfred O. Hero
The problem of f-divergence estimation is important in the fields of machine learning, information theory, and statistics. While several nonparametric divergence estimators exist,…
Ensemble estimation of multivariate f-divergence
Kevin R. Moon, Alfred O. Hero
f-divergence estimation is an important problem in the fields of information theory, machine learning, and statistics. While several divergence estimators exist, relatively few of…