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New research details Wasserstein distance for parameter estimation complexity

By PulseAugur Editorial · [1 sources] ·

This paper explores the complexity of estimating translation and shrinkage parameters for distributions, a fundamental challenge in machine learning. While Maximum Likelihood Estimation (MLE) for this problem is NP-hard, the research demonstrates that $\varepsilon$-approximations can be achieved in polynomial time using the Wasserstein distance. The findings are relevant for tasks such as fitting neural network weights and Bayesian inference. AI

IMPACT Introduces a novel approach to parameter estimation complexity, potentially improving efficiency in machine learning tasks.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details Wasserstein distance for parameter estimation complexity

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Valentio Iverson, Stephen Vavasis ·

    Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization

    arXiv:2501.10172v2 Announce Type: replace Abstract: Parameter estimation is a fundamental challenge in machine learning, crucial for tasks such as neural network weight fitting and Bayesian inference. This paper focuses on the complexity of estimating translation $\boldsymbol{\mu…

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