Average-Case to (shifted) Worst-Case Reduction for the Trace Reconstruction Problem
Ittai Rubinstein

TL;DR
This paper introduces a reduction from average-case to worst-case trace reconstruction, improving the sample complexity for average-case reconstruction and extending Chase's worst-case bounds to the insertion-deletion channel.
Contribution
It presents a novel reduction technique connecting average-case and worst-case trace reconstruction, leading to improved algorithms and bounds for the insertion-deletion channel.
Findings
Improved average-case sample complexity to ^{ ilde{O}( ext{log}^{1/5} n)}.
Chase's worst-case upper bound applies to the insertion-deletion channel.
Established a reduction framework for trace reconstruction problems.
Abstract
The {\em insertion-deletion channel} takes as input a binary string , and outputs a string where some of the bits have been deleted and others inserted independently at random. In the {\em trace reconstruction problem}, one is given many outputs (called {\em traces}) of the insertion-deletion channel on the same input message , and asked to recover the input message. Nazarov and Peres (STOC 2017), and De, O'Donnell and Servedio (STOC 2017) showed that any string can be reconstructed from traces. Holden, Pemantle, Peres and Zhai (COLT 2018) adapt the techniques used to prove this upper bound, to an algorithm for the average-case trace reconstruction with a sample complexity of . However, it is not clear how to apply their techniques more generally and in particular for the recent worst-case upper bound of…
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