Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications

31 May 2017

Papers citing "Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications"

13 / 13 papers shown

Title
Efficient Hamiltonian, structure and trace distance learning of Gaussian states Marco Fanizza Cambyse Rouzé Daniel Stilck França 52 6 0 05 Nov 2024
Discrete distributions are learnable from metastable samples Abhijith Jayakumar A. Lokhov Sidhant Misra Marc Vuffray 169 1 0 17 Oct 2024
Structure learning of Hamiltonians from real-time evolution Ainesh Bakshi Allen Liu Ankur Moitra Ewin Tang 47 13 0 30 Apr 2024
Learning Graphical Models Using Multiplicative Weights Adam R. Klivans Raghu Meka 53 113 0 20 Jun 2017
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray Sidhant Misra A. Lokhov Michael Chertkov 36 111 0 24 May 2016
Active Learning Algorithms for Graphical Model Selection Gautam Dasarathy Aarti Singh Maria-Florina Balcan Jonghyuk Park 135 25 0 01 Feb 2016
Efficiently learning Ising models on arbitrary graphs Guy Bresler 65 203 0 22 Nov 2014
Learning Polytrees S. Dasgupta TPM 107 129 0 23 Jan 2013
Maximum Likelihood Bounded Tree-Width Markov Networks Nathan Srebro TPM 107 128 0 10 Jan 2013
High-dimensional structure estimation in Ising models: Local separation criterion Anima Anandkumar Vincent Y. F. Tan Furong Huang A. Willsky 143 114 0 08 Jul 2011
$High-dimensional Ising model selection using ${\ell_1}$-regularized logistic regression$ High-dimensional Ising model selection using ${\ell_1}$ -regularized logistic regression Pradeep Ravikumar Martin J. Wainwright John D. Lafferty 246 957 0 02 Oct 2010
Information-theoretic limits of selecting binary graphical models in high dimensions N. Santhanam Martin J. Wainwright 130 204 0 16 May 2009
Reconstruction of Markov Random Fields from Samples: Some Easy Observations and Algorithms Guy Bresler Elchanan Mossel Allan Sly 93 159 0 10 Dec 2007