Low Degree Hardness for Broadcasting on Trees
Han Huang, Elchanan Mossel

TL;DR
This paper proves that below the Kesten-Stigum bound, low-degree polynomial algorithms cannot effectively infer the root in broadcasting on trees, highlighting inherent computational hardness in this regime.
Contribution
It establishes the first low-degree polynomial lower bounds for broadcasting on trees outside product measure settings, confirming computational hardness below the Kesten-Stigum bound.
Findings
Any $O(\log n)$ degree polynomial has vanishing correlation with the root below the Kesten-Stigum bound.
The result extends low-degree hardness to non-product measure settings in broadcasting problems.
Supports the conjecture that efficient algorithms cannot succeed below the Kesten-Stigum threshold.
Abstract
We study the low-degree hardness of broadcasting on trees. Broadcasting on trees has been extensively studied in statistical physics, in computational biology in relation to phylogenetic reconstruction and in statistics and computer science in the context of block model inference, and as a simple data model for algorithms that may require depth for inference. The inference of the root can be carried by celebrated Belief Propagation (BP) algorithm which achieves Bayes-optimal performance. Despite the fact that this algorithm runs in linear time (using real operations), recent works indicated that this algorithm in fact requires high level of complexity. Moitra, Mossel and Sandon constructed a chain for which estimating the root better than random (for a typical input) is complete. Kohler and Mossel constructed chains such that for trees with leaves, recovering the root better…
Peer Reviews
Decision·NeurIPS 2024 poster
Previous work had asked the question whether the KS-threshold is the correct threshold for efficient algorithms. This work answers this question in the affirmative. The ideas used in the proof are novel and clever.
The first part of the introduction is a bit hard to follow for non-experts, maybe some additional context would help.
NA
NA
The paper shows a very nice result, and I feel like the overview in the main body gives a good idea of how the proof of the result proceeds.
There are no real weaknesses to the paper, however due to space constraints it is not really possible to ascertain the claimed results from the main body of the paper. I could not check the appendix carefully.
Broadcasting is a well studied and useful model. The question of how well low-degree statistics of the leaves correlate with the root is extremely natural mathematically, and is supported by a number of recent works showing the general power of low-degree polynomials in predicting statistical-computational gaps. The only known bounds for this general problem were for the very specialized $\lambda=0$ case. The techniques introduced by the authors, including the notion of fractal capacity (a more
I do not see any substantial weaknesses in this work. One could of course ask for lower bounds against super-logarithmic degree polynomials as in known in the $\lambda=0$ case, but the results in this paper still mark a major step forward on this problem. I would request the authors run a grammar/syntax check. There are a huge number of typographical/syntax errors that slow down the reading of the work to some extent.
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Taxonomy
TopicsVLSI and FPGA Design Techniques · Low-power high-performance VLSI design · Interconnection Networks and Systems
