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Sparse covariance estimation bounds show efficiency gains

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new information-theoretic bounds for sparse covariance estimation in a distributed vertical-split model. Their work demonstrates that imposing sparsity on the cross-covariance matrix can significantly reduce communication and sample complexity compared to dense matrices. This improvement is particularly notable in the 1-sparse case, offering an exponential gain in efficiency. AI

IMPACT Establishes theoretical efficiency gains for distributed machine learning algorithms dealing with sparse data.

RANK_REASON The cluster contains an academic paper detailing new theoretical bounds for a statistical estimation problem.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Sparse covariance estimation bounds show efficiency gains

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The cluster contains an academic paper detailing new theoretical bounds for a statistical estimation problem.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Jing Yee Tan, Guangyue Han ·

    Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

    arXiv:2606.07124v1 Announce Type: cross Abstract: We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of $m$ i.i.d. sub-Gaussian samples and communica…

  2. arXiv stat.ML TIER_1 English(EN) · Guangyue Han ·

    Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

    We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of $m$ i.i.d. sub-Gaussian samples and communicate a limited number of bits to a central server. W…

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