Sign-Rank of $k$-Hamming Distance is Constant
Mika G\"o\"os, Nathaniel Harms, Valentin Imbach, Dmitry Sokolov

TL;DR
This paper proves that the sign-rank of the $k$-Hamming Distance matrix is bounded by a constant depending only on $k$, refuting previous conjectures that it depends on the number of bits $n$, with implications for communication complexity.
Contribution
It establishes a constant upper bound on the sign-rank of $k$-Hamming Distance matrices, challenging prior beliefs about their dependence on $n$, and introduces techniques applicable to related matrices.
Findings
Sign-rank of $k$-Hamming Distance matrix is $2^{O(k)}$
Refutes the conjecture that sign-rank depends on $n$
Provides bounds for related large-margin matrices
Abstract
We prove that the sign-rank of the -Hamming Distance matrix on bits is , independent of the number of bits . This strongly refutes the conjecture of Hatami, Hatami, Pires, Tao, and Zhao (RANDOM 2022), and Hatami, Hosseini, and Meng (STOC 2023), repeated in several other papers, that the sign-rank should depend on . This conjecture would have qualitatively separated margin from sign-rank (or, equivalently, bounded-error from unbounded-error randomized communication). In fact, our technique gives constant sign-rank upper bounds for all matrices which reduce to -Hamming Distance, as well as large-margin matrices recently shown to be irreducible to -Hamming Distance.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Algorithms and Data Compression
