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This is a Quantitative Study study.

October 16, 2025Open Access

Allocating Variance to Maximize Expectation

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Authors

RLRenato Paes LemeGoogle (United States)CSClifford SteinDartmouth College YTYifeng TengGoogle (United States)

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Implication

This research demonstrates variance allocation strategies to enhance expectation in Gaussian models, suggesting practical applications in utility maximization and learning models.

Key Points

The optimal variance allocation concentrates on a small variable subset, enhancing efficacy.
A polynomial time approximation scheme effectively computes expectation for single Gaussian variables.
An O(log n) algorithm is developed for general cases with multiple Gaussian variables.
Findings indicate wide applicability, from auctions to mixture models in genetics.

Cite This Study

Leme et al. (2025) studied this question.

synapsesocial.com/papers/68f0f51d8dd8ea469b1d6e41 https://doi.org/10.48550/arxiv.2502.18463

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Implication

This research demonstrates variance allocation strategies to enhance expectation in Gaussian models, suggesting practical applications in utility maximization and learning models.

Allocating Variance to Maximize Expectation

Discussion

Member takes

Implication

Key Points

Cite This Study

Allocating Variance to Maximize Expectation

Discussion

Member takes

Implication

Key Points

Cite This Study

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