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Summary of A Biased Estimator For Minmax Sampling and Distributed Aggregation, by Joel Wolfrath and Abhishek Chandra


A Biased Estimator for MinMax Sampling and Distributed Aggregation

by Joel Wolfrath, Abhishek Chandra

First submitted to arxiv on: 26 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Applications (stat.AP)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper proposes a biased version of the MinMax sampling technique, called B-MinMax, which reduces variance at the cost of increasing estimator bias. The authors prove that when no aggregation is performed, B-MinMax achieves a strictly lower mean squared error (MSE) compared to the unbiased MinMax estimator. When aggregation is required, B-MinMax is preferred for small sample sizes or limited aggregated vectors. Experimental results demonstrate significant MSE reduction in practical settings.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper develops a new way to reduce data before sending it over slow networks. The method, called MinMax, makes sure that the maximum difference between different parts of the data is minimized. This helps get more accurate estimates when combining data from multiple sources. The authors introduce a new version of this technique, B-MinMax, which sacrifices some accuracy for even less variation in the data. They show that this approach can be better than the original MinMax method in many cases.

Keywords

» Artificial intelligence  » Mse