mlsanityData to 7 Oct 2026

Robust Mean Estimation under Quantization

arXiv 2601.07074 ↗First posted 11 Jan 2026stat.ML cs.LG math.STcited by 4

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Abstract (arXiv)

We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a single sample, and the second is a partial quantization setting, in which the estimator may use a small fraction of unquantized data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 11 Jan 2026.

Builds on

11 of 28 references.

References · 11

  1. 2019Robust subgaussian estimation of a mean vector in nearly linear timecited by 37
  2. 2020Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithmscited by 16
  3. 2022Correlated quantization for distributed mean estimation and optimizationcited by 14
  4. 2022Covariance Estimation: Optimal Dimension-free Guarantees for Adversarial Corruption and Heavy Tailscited by 14
  5. 2022High Dimensional Statistical Estimation under Uniformly Dithered One-bit Quantizationcited by 11
  6. 2018The Generalized Lasso for Sub-gaussian Measurements with Dithered Quantizationcited by 8
  7. 2019Mean Estimation from One-Bit Measurementscited by 8
  8. 2025Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexitycited by 5
  9. 2021Distributed Sparse Normal Means Estimation with Sublinear Communicationcited by 3
  10. 2014On Quantizer Design for Distributed Bayesian Estimation in Sensor Networkscited by 2
  11. 2023A Parameter-Free Two-Bit Covariance Estimator with Improved Operator Norm Error Ratecited by 2

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Citing papers per year: 2026: 4.

4 papers.