TY - GEN
T1 - Tightening fractional covering upper bounds on the partition function for high-order region graphs
AU - Hazan, Tamir
AU - Peng, Jian
AU - Shashua, Amnon
PY - 2012
Y1 - 2012
N2 - In this paper we present a new approach for tightening upper bounds on the partition function. Our upper bounds are based on fractional covering bounds on the entropy function, and result in a concave program to compute these bounds and a convex program to tighten them. To solve these programs effectively for general region graphs we utilize the entropy barrier method, thus decomposing the original programs by their dual programs and solve them with dual block optimization scheme. The entropy barrier method provides an elegant framework to generalize the message-passing scheme to high-order region graph, as well as to solve the block dual steps in closed-form. This is a key for computational relevancy for large problems with thousands of regions.
AB - In this paper we present a new approach for tightening upper bounds on the partition function. Our upper bounds are based on fractional covering bounds on the entropy function, and result in a concave program to compute these bounds and a convex program to tighten them. To solve these programs effectively for general region graphs we utilize the entropy barrier method, thus decomposing the original programs by their dual programs and solve them with dual block optimization scheme. The entropy barrier method provides an elegant framework to generalize the message-passing scheme to high-order region graph, as well as to solve the block dual steps in closed-form. This is a key for computational relevancy for large problems with thousands of regions.
UR - http://www.scopus.com/inward/record.url?scp=84886066594&partnerID=8YFLogxK
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AN - SCOPUS:84886066594
SN - 9780974903989
T3 - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
SP - 356
EP - 366
BT - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
T2 - 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
Y2 - 15 August 2012 through 17 August 2012
ER -