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New research shows non-adaptive protocols match adaptive rates in 1-bit mean estimation

By PulseAugur Editorial · [2 sources] ·

A new research paper explores non-adaptive protocols for distributed mean estimation with a 1-bit communication constraint. The study demonstrates that a non-adaptive approach can achieve the same optimal rate as adaptive methods, challenging previous assumptions about the necessity of multi-stage interaction. The research also quantifies the trade-off between sample complexity and interval constraints in these estimators. AI

IMPACT This research contributes to the theoretical understanding of distributed machine learning under communication constraints, potentially influencing future algorithm design.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new theoretical finding in machine learning.

Read on Hugging Face Daily Papers →

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

New research shows non-adaptive protocols match adaptive rates in 1-bit mean estimation

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ivan Lau, Jonathan Scarlett ·

    Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff

    arXiv:2609.08564v1 Announce Type: cross Abstract: We study distributed one-dimensional mean estimation under a 1-bit communication constraint. Each agent observes one sample, drawn independently from an unknown distribution, and returns a single bit in response to a query $Q: \ma…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff

    We study distributed one-dimensional mean estimation under a 1-bit communication constraint. Each agent observes one sample, drawn independently from an unknown distribution, and returns a single bit in response to a query $Q: \mathbb{R}\to\{0,1\}$ chosen by a central learner. Th…

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