TY - JOUR

T1 - Lower bounds for testing graphical models

T2 - Colorings and antiferromagnetic ising models

AU - Bezáková, Ivona

AU - Blanca, Antonio

AU - Chen, Zongchen

AU - Stefankovič, Daniel

AU - Vigoda, Eric

N1 - Funding Information:
Research supported in part by NSF grants 1819546, CCF-1850443, CCF-1617306, CCF-1563838 and CCF-1563757.
Publisher Copyright:
© 2020 Ivona Bezáková, Antonio Blanca, Zongchen Chen, Daniel Štefankovič, Eric Vigoda. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v21/19-580.html.

PY - 2020/2/1

Y1 - 2020/2/1

N2 - We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model M and a sampling oracle for the distribution µM∗ of an unknown model M∗, can we efficiently determine if the two models M and M∗ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. Specifically, for n-vertex graphs of maximum degree d, we prove that if |β|d = ω(log n) (where β is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless RP = NP. In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs.

AB - We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model M and a sampling oracle for the distribution µM∗ of an unknown model M∗, can we efficiently determine if the two models M and M∗ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. Specifically, for n-vertex graphs of maximum degree d, we prove that if |β|d = ω(log n) (where β is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless RP = NP. In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs.

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M3 - Article

AN - SCOPUS:85086803868

VL - 21

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

SN - 1532-4435

ER -