mlsanityData to 7 Oct 2026

Efficient median of means estimator

arXiv 2305.18681 ↗First posted 30 May 2023math.ST stat.ML stat.THcited by 7

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Abstract (arXiv)

The goal of this note is to present a modification of the popular median of means estimator that achieves sub-Gaussian deviation bounds with nearly optimal constants under minimal assumptions on the underlying distribution. We build on a recent work on the topic by the author, and prove that desired guarantees can be attained under weaker requirements.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 1 of its 14 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

1 of 14 references.

References · 1

  1. 2019Mean estimation and regression under heavy-tailed distributions--a surveycited by 133

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2025Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexitycited by 5
  2. 2026Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimescited by 5
  3. 2024Multiple-policy Evaluation via Density Estimationcited by 2
  4. 2026Open Problem: Is Interaction Necessary for Order-Optimal 1-bit Mean Estimation?cited by 2
  5. 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
  6. 2025On the Optimality of the Median-of-Means Estimator under Adversarial Contaminationcited by 0
  7. 2026Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oraclecited by 0