Learning Ising models from one or multiple samples

Author:

Dagan Yuval1ORCID,Daskalakis Constantinos1,Dikkala Nishanth2,Kandiros Anthimos Vardis1

Affiliation:

1. Massachusetts Institute of Technology, USA

2. Google, USA / Massachusetts Institute of Technology, USA

Publisher

ACM

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient learning of ground and thermal states within phases of matter;Nature Communications;2024-09-05

2. Exploring the nexus between thermodynamic phase transitions and geometric fractals through systematic lattice point classification;AIP Advances;2024-08-01

3. A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

4. Concentration inequalities for some negatively dependent binary random variables;Latin American Journal of Probability and Mathematical Statistics;2023

5. Estimation in tensor Ising models;Information and Inference: A Journal of the IMA;2022-06-03

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