mlsanityData to 7 Oct 2026

Collaborative Algorithms for Online Personalized Mean Estimation

arXiv 2208.11530 ↗First posted 24 Aug 2022cs.LG stat.MLcited by 5

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Abstract (arXiv)

We consider an online estimation problem involving a set of agents. Each agent has access to a (personal) process that generates samples from a real-valued distribution and seeks to estimate its mean. We study the case where some of the distributions have the same mean, and the agents are allowed to actively query information from other agents. The goal is to design an algorithm that enables each agent to improve its mean estimate thanks to communication with other agents. The means as well as the number of distributions with same mean are unknown, which makes the task nontrivial. We introduce a novel collaborative strategy to solve this online personalized mean estimation problem. We analyze its time complexity and introduce variants that enjoy good performance in numerical experiments. We also extend our approach to the setting where clusters of agents with similar means seek to estimate the mean of their cluster.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

23 of 38 references.

References · 23

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11 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Online Learning from Strategic Human Feedback in LLM Fine-Tuningcited by 10
  2. 2024Scalable Decentralized Algorithms for Online Personalized Mean Estimationcited by 2
  3. 2024Differentially-Private Collaborative Online Personalized Mean Estimationcited by 1
  4. 2025Communication-Constrained Private Decentralized Online Personalized Mean Estimationcited by 0
  5. 2026Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcingcited by 0