Fetching the paper…
Reading the bibliography…
Motivated by a sampling problem basic to computational statistical inference, we develop a nearly optimal algorithm for a fundamental problem in spectral graph theory and numerical analysis.
Combinatorial preconditioners for sparse, symmetric, diagonally dominant linear systems
Keith D Gremban · 1996
Earlier work this paper cites.
Learning in Graphical Models
M. I. Jordan · 1998
Earlier work this paper cites.
An introduction to MCMC for machine learning
Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan · 2003
Earlier work this paper cites.
Efficient multiscale sampling from products of gaussian mixtures
Alexander Ihler, Er T. Ihler, Erik Sudderth, William Freeman, and Alan Willsky · 2003
Earlier work this paper cites.
Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
Daniel A. Spielman and Shang-Hua Teng · 2004
Earlier work this paper cites.
Pattern recognition and machine learning
Christopher M. Bishop · 2006
Earlier work this paper cites.
Positive definite matrices
Rajendra Bhatia · 2007
Earlier work this paper cites.
Faster approximate lossy generalized flow via interior point algorithms
Samuel I. Daitch and Daniel A. Spielman · 2008
Earlier work this paper cites.
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Daphne Koller and Nir Friedman · 2009
Earlier work this paper cites.
Approaching optimality for solving sdd linear systems
Ioannis Koutis, Gary L. Miller, and Richard Peng · 2010
Cited alongside, same era.
Gaussian sampling by local perturbations
George Papandreou and Alan L. Yuille · 2010
Cited alongside, same era.
Parallel gibbs sampling: From colored fields to thin junction trees
Joseph Gonzalez, Yucheng Low, Arthur Gretton, and Carlos Guestrin · 2011
Cited alongside, same era.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Geng Niu, Benjamin Recht, Christopher Re, and Stephen J. Wright · 2011
Cited alongside, same era.
Graph sparsification by effective resistances
Daniel A. Spielman and Nikil Srivastava · 2011
Cited alongside, same era.
Spectral sparsification of graphs
Daniel A. Spielman and Shang-Hua Teng · 2011
Cited alongside, same era.
Sampling from gaussian graphical models using subgraph perturbations
Ying Liu, Oliver Kosut, and Alan S. Willsky · 2013
Later among the works it cites.
Improved parallel algorithms for spanners and hopsets
Gary L. Miller, Richard Peng, and Shen Chen Xu · 2013
Later among the works it cites.
Algorithm Design Using Spectral Graph Theory
Richard Peng · 2013
Later among the works it cites.
Distributed stochastic gradient MCMC
Sungjin Ahn, Babak Shahbaba, and Max Welling · 2014
Closest in time.
Preconditioned krylov subspace methods for sampling multivariate gaussian distributions
Edmond Chow and Yousef Saad · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses
Po-Ling Loh and Martin J. Wainwright · 2012
Cited alongside, same era.
Analyzing hogwild parallel gaussian gibbs sampling
Matthew Johnson, James Saunderson, and Alan Willsky · 2013
Cited alongside, same era.
A simple, combinatorial algorithm for solving sdd systems in nearly-linear time
Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, and Zeyuan Allen Zhu · 2013
Cited alongside, same era.
Ioannis Koutis · 2014
Closest in time.
An efficient parallel solver for sdd linear systems
Richard Peng and Daniel A. Spielman · 2014
Closest in time.
Daniel A. Spielman and Shang-Hua Teng · 2014
Closest in time.