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
