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Researchers develop algorithms for total variation distance between product distribution mixtures

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed algorithms to approximate the total variation distance between mixtures of product distributions. The work focuses on an n-dimensional discrete domain and provides a randomized algorithm for approximation within a $(1 \pm \varepsilon)$ error. For mixtures of Boolean subcubes, a deterministic algorithm offers exact computation, though the problem is shown to be #P-hard under certain conditions. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Provides theoretical advancements in understanding and computing distances between complex probability distributions, relevant for generative modeling and data analysis.

RANK_REASON This is a research paper detailing new algorithms for computing distances between probability distributions.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Weiming Feng, Yucheng Fu, Minji Yang, Anqi Zhang ·

    On Computing Total Variation Distance Between Mixtures of Product Distributions

    arXiv:2605.03839v1 Announce Type: cross Abstract: We study the problem of approximating the total variation distance between two mixtures of product distributions over an $n$-dimensional discrete domain. Given two mixtures $\mathbb{P}$ and $\mathbb{Q}$ with $k_1$ and $k_2$ produc…

  2. arXiv cs.LG TIER_1 · Anqi Zhang ·

    On Computing Total Variation Distance Between Mixtures of Product Distributions

    We study the problem of approximating the total variation distance between two mixtures of product distributions over an $n$-dimensional discrete domain. Given two mixtures $\mathbb{P}$ and $\mathbb{Q}$ with $k_1$ and $k_2$ product distributions over $[q]^n$, respectively, we giv…

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