2019 COLT COLT 2019

Reconstructing Trees from Traces

Abstract

We study the problem of learning a node-labeled tree given independent traces from an appropriately defined deletion channel. This problem, tree trace reconstruction, generalizes string trace reconstruction, which corresponds to the tree being a path. For many classes of trees, including complete trees and spiders, we provide algorithms that reconstruct the labels using only a polynomial number of traces. This exhibits a stark contrast to known results on string trace reconstruction, which require exponentially many traces, and where a central open problem is to determine whether a polynomial number of traces suffice. Our techniques combine novel combinatorial and complex analytic methods.

🧭 Keyword Pioneer — combinatorial method
🐝 Cross-Pollinator — Computer Science, Computer Vision, Data Science & Analytics, Machine Learning, Mathematics & Optimization, Natural Language Processing