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Soft Guessing Under Logarithmic Loss Allowing Errors and Variable-Length Source Coding

Published: October 10, 2025 | arXiv ID: 2510.09015v1

By: Shota Saito, Hamdi Joudeh

Potential Business Impact:

Helps guess secrets faster with fewer mistakes.

Business Areas:
A/B Testing Data and Analytics

This paper considers the problem of soft guessing under a logarithmic loss distortion measure while allowing errors. We find an optimal guessing strategy, and derive single-shot upper and lower bounds for the minimal guessing moments as well as an asymptotic expansion for i.i.d. sources. These results are extended to the case where side information is available to the guesser. Furthermore, a connection between soft guessing allowing errors and variable-length lossy source coding under logarithmic loss is demonstrated. The R\'enyi entropy, the smooth R\'enyi entropy, and their conditional versions play an important role.

Country of Origin
🇳🇱 🇯🇵 Netherlands, Japan

Page Count
25 pages

Category
Computer Science:
Information Theory