TY - GEN

T1 - Hardness of reconstructing multivariate polynomials over finite fields

AU - Gopalan, Parikshit

AU - Khot, Subhash

AU - Saket, Rishi

PY - 2007

Y1 - 2007

N2 - We study the polynomial reconstruction problem for low-degree multivariate polynomials over double-struck F sign[2]. In this problem, we are given a set of points x ∈{0, 1}n and target values f(x) ∈ {0, 1} for each of these points, with the promise that there is a polynomial over double-struck F sign[2] of degree at most d that agrees with f at 1 - ε fraction of the points. Our goal is to find a degree d polynomial that has good agreement with f. We show that it is NP-hard to find a polynomial that agrees with f on more than 1 - 2-d + δ fraction of the points for any ε, δ > 0. This holds even with the stronger promise that the polynomial that fits the data is in fact linear, whereas the algorithm is allowed to find a polynomial of degree d. Previously the only known hardness of approximation (or even NP-completeness) was for the case when d = 1, which follows from a celebrated result of Håstad [16]. In the setting of Computational Learning, our result shows the hardness of (non-proper)agnostic learning of parities, where the learner is allowed a low-degree polynomial over double-struck F sign[2] as a hypothesis. This is the first nonproper hardness result for this central problem in computational learning. Our results extend to multivariate polynomial reconstruction over any finite field.

AB - We study the polynomial reconstruction problem for low-degree multivariate polynomials over double-struck F sign[2]. In this problem, we are given a set of points x ∈{0, 1}n and target values f(x) ∈ {0, 1} for each of these points, with the promise that there is a polynomial over double-struck F sign[2] of degree at most d that agrees with f at 1 - ε fraction of the points. Our goal is to find a degree d polynomial that has good agreement with f. We show that it is NP-hard to find a polynomial that agrees with f on more than 1 - 2-d + δ fraction of the points for any ε, δ > 0. This holds even with the stronger promise that the polynomial that fits the data is in fact linear, whereas the algorithm is allowed to find a polynomial of degree d. Previously the only known hardness of approximation (or even NP-completeness) was for the case when d = 1, which follows from a celebrated result of Håstad [16]. In the setting of Computational Learning, our result shows the hardness of (non-proper)agnostic learning of parities, where the learner is allowed a low-degree polynomial over double-struck F sign[2] as a hypothesis. This is the first nonproper hardness result for this central problem in computational learning. Our results extend to multivariate polynomial reconstruction over any finite field.

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U2 - 10.1109/FOCS.2007.4389506

DO - 10.1109/FOCS.2007.4389506

M3 - Conference contribution

AN - SCOPUS:46749152630

SN - 0769530109

SN - 9780769530109

T3 - Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS

SP - 349

EP - 359

BT - Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007

T2 - 48th Annual Symposium on Foundations of Computer Science, FOCS 2007

Y2 - 20 October 2007 through 23 October 2007

ER -