On Multilinear Forms: Bias, Correlation, and Tensor Rank
Abhishek Bhrushundi, Prahladh Harsha, Pooya Hatami, Swastik Kopparty,, Mrinal Kumar

TL;DR
This paper establishes new bounds relating the bias, correlation, and tensor rank of multilinear forms over GF(2), providing insights into tensor complexity and lower bounds for tensor rank in specific cases.
Contribution
It introduces novel relationships between tensor rank, bias, and correlation of multilinear forms, including a new lower bound for the tensor rank of finite field multiplication.
Findings
Random d-linear forms have exponentially low correlation with low-degree polynomials.
Small tensor rank implies large bias in associated multilinear forms.
Proved a new lower bound of 3.52k for the tensor rank of finite field multiplication tensor.
Abstract
In this paper, we prove new relations between the bias of multilinear forms, the correlation between multilinear forms and lower degree polynomials, and the rank of tensors over . We show the following results for multilinear forms and tensors. 1. Correlation bounds : We show that a random -linear form has exponentially low correlation with low-degree polynomials. More precisely, for , we show that a random -linear form has correlation with any polynomial of degree at most . This result is proved by giving near-optimal bounds on the bias of random -linear form, which is in turn proved by giving near-optimal bounds on the probability that a random rank- -linear form is identically zero. 2. Tensor-rank vs Bias : We show that if a -dimensional…
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