A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width
Author:
Affiliation:
1. Massachusetts Institute of Technology, Cambridge, USA
Funder
Office of Naval Research
Simons Foundation
National Science Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3618260.3649674
Reference56 articles.
1. Arka Adhikari Christian Brennecke Changji Xu and Horng-Tzer Yau. 2024. Spectral gap estimates for mixed p-spin models at high temperature. Probability Theory and Related Fields isbn:1432-2064 https://doi.org/10.1007/s00440-024-01261-9 10.1007/s00440-024-01261-9
2. Bounds on the covariance matrix of the Sherrington–Kirkpatrick model
3. Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization
4. Learning loopy graphical models with latent variables: Efficient methods and guarantees
5. Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses
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