AI RESEARCH
Estimating Mixture Distributions via Stochastic Mirror Descent
arXiv CS.LG
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ArXi:2605.24929v1 Announce Type: cross We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss. Framing the task as a stochastic convex optimization problem over the space of $ M $-component mixture distributions, we propose a family of estimators derived from the stochastic mirror descent (SMD) algorithm.