Fetching the paper…
Reading the bibliography…
We give a simple, multiplicative-weight update algorithm for learning undirected graphical models or Markov random fields (MRFs).
Approximating discrete probability distributions with dependence trees
C. Chow and C. Liu · 1968
Earlier work this paper cites.
Learning and relearning in boltzmann machines
G. Hinton and T. Sejnowski · 1986
Earlier work this paper cites.
Functionality in neural nets
L. G. Valiant · 1988
Earlier work this paper cites.
Markov random fields in statistics
P. Clifford · 1990
Earlier work this paper cites.
Three XOR-lemmas - an exposition
Oded Goldreich · 1995
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert Schapire · 1997
Earlier work this paper cites.
Graphical Models
S. L. Lauritzen · 1998
Earlier work this paper cites.
Factor graphs and the sum-product algorithm
Frank R. Kschischang, Brendan J. Frey, and Hans-Andrea Loeliger · 2001
Earlier work this paper cites.
Learning markov networks: Maximum bounded tree-width graphs
Karger and Srebro · 2001
Earlier work this paper cites.
Learning factor graphs in polynomial time and sample complexity
Pieter Abbeel, Daphne Koller, and Andrew Y. Ng · 2006
Earlier work this paper cites.
Towards an integrated protein-protein interaction network: A relational markov network approach
Ariel Jaimovich, Gal Elidan, Hanah Margalit, and Nir Friedman · 2006
Earlier work this paper cites.
High-dimensional graphical model selection using ℓ \ell 1 -regularized logistic regression
Martin J. Wainwright, Pradeep Ravikumar, and John D. Lafferty · 2006
Cited alongside, same era.
On agnostic learning of parities, monomials, and halfspaces
Vitaly Feldman, Parikshit Gopalan, Subhash Khot, and Ashok Kumar Ponnuswami · 2009
Cited alongside, same era.
Hardness of learning halfspaces with noise
Venkatesan Guruswami and Prasad Raghavendra · 2009
Cited alongside, same era.
Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman · 2009
Cited alongside, same era.
The isotron algorithm: High-dimensional isotonic regression
Adam Tauman Kalai and Ravi Sastry · 2009
Cited alongside, same era.
Learning in markov random fields using tempered transitions
Ruslan Salakhutdinov · 2009
Cited alongside, same era.
Greedy learning of graphical models with small girth
Avik Ray, Sujay Sanghavi, and Sanjay Shakkottai · 2012
Later among the works it cites.
Information-theoretic limits of selecting binary graphical models in high dimensions
Narayana P. Santhanam and Martin J. Wainwright · 2012
Later among the works it cites.
Reconstruction of markov random fields from samples: Some observations and algorithms
Guy Bresler, Elchanan Mossel, and Allan Sly · 2013
Later among the works it cites.
Robust estimation of latent tree graphical models: Inferring hidden states with inexact parameters
Elchanan Mossel, Sébastien Roch, and Allan Sly · 2013
Later among the works it cites.
Structure learning of antiferromagnetic ising models
Guy Bresler, David Gamarnik, and Devavrat Shah · 2014
Later among the works it cites.
Learning graphs with a few hubs
Rashish Tandon and Pradeep Ravikumar · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, and Bin Yu · 2010
Cited alongside, same era.
High-dimensional structure estimation in ising models: Local separation criterion
Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, and Alan S. Willsky · 2011
Cited alongside, same era.
Efficient learning of generalized linear and single index models with isotonic regression
Sham M. Kakade, Adam Kalai, Varun Kanade, and Ohad Shamir · 2011
Cited alongside, same era.
Greedy learning of markov network structure, 2012
Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi, and Sanjay Shakkottai · 2012
Cited alongside, same era.
Later among the works it cites.
Efficiently learning ising models on arbitrary graphs
Guy Bresler · 2015
Later among the works it cites.
Finding correlations in subquadratic time, with applications to learning parities and the closest pair problem
Gregory Valiant · 2015
Later among the works it cites.
Interaction screening: Efficient and sample-optimal learning of ising models
Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, and Michael Chertkov · 2016
Later among the works it cites.
Information theoretic properties of markov random fields, and their algorithmic applications
Linus Hamilton, Frederic Koehler, and Ankur Moitra · 2017
Closest in time.