A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.
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Semiparametric Elliptical Mixture Clustering for High-Dimensional Data
A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
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Optimal Policy Learning under Budget and Coverage Constraints
Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.