Learning Ising models from one or multiple samples

Author(s)
Dagan, Yuval; Daskalakis, Constantinos; Dikkala, Nishanth; Kandiros, Anthimos Vardis
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Date issued
2021
URI
https://hdl.handle.net/1721.1/143465.2
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
Publisher
Association for Computing Machinery (ACM)
Citation
Dagan, Yuval, Daskalakis, Constantinos, Dikkala, Nishanth and Kandiros, Anthimos Vardis. 2021. "Learning Ising models from one or multiple samples." Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing.
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21721.1/143465.2*2022-10-27T18:47:36ZFile Update: final published version
11721.1/1434652022-06-17T16:15:43Z

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