ACM Symposium on Theory of Computing, STOC 2015


Article Details
Title: Efficiently Learning Ising Models on Arbitrary Graphs
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Authors: Guy Bresler
  • Massachusetts Institute of Technology, Laboratory for Information and Decision Systems Department of EECS
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NSF Award Numbers: 1462158, 1335155, 1161964
DBLP Key: conf/stoc/Bresler15
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