arXiv Machine Learning August 6, 2026 By Kazu Ghalamkari, Jesper L{\o}ve Hinrich, Morten M{\o}rup

E$^2$M: Double Bounded $\alpha$-Divergence Optimization for Tensor-based Discrete Density Estimation

Read the original on arXiv Machine Learning →

arXiv:2405. 18220v4 Announce Type: replace-cross Abstract: Tensor-based discrete density estimation requires flexible modeling and proper divergence criteria to enable effective learning; however, traditional approaches using $\alpha$-divergence face analytical challenges due to the $\alpha$-power terms in the objective function, which hinder the derivation of closed-form update rules.

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