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Bernstein’s Concentration Inequality In Equivalence Testing
by Computational Technology for AllDecember 9th, 2024
Too Long; Didn't Read
The paper provides Proof of Claim using concentration inequality.
Authors:
(1) Diptarka Chakraborty, National University of Singapore, Singapore
(2) Sourav Chakraborty, Indian Statistical Institute, Kolkata;
(3) Gunjan Kumar, National University of Singapore, Singapore;
(4) Kuldeep S. Meel, University of Toronto, Toronto.
Table of Links
4 An Efficient One-Round Adaptive Algorithm and 4.1 High-Level Overview
6 Acknowledgements and References
B An O(log log n)-query fully adaptive algorithm
A MISSING PROOFS
Therefore, by additive Chernoff bound (Lemma 2.3), the value et = EstTail(D, i, S, β, b, m) returned by the algorithm satisfies
To prove Claims 4.10, 4.11 and 4.12, we will use the following concentration inequality that directly follows from Bernstein’s concentration inequality.
This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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