Jason K. Johnson

22 papers A* 1A 1B 3Journal 11Unranked 5
YearRankTypeTitle / Venue / Authors
2016 J jnl
Learning Planar Ising Models.
J. Mach. Learn. Res.
Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli
2013 B conf
Improved linear programming decoding using frustrated cycles.
ISIT
Shrinivas Kudekar, Jason K. Johnson, Misha Chertkov
2011 J jnl
Improved Linear Programming Decoding using Frustrated Cycles
CoRR
Shrinivas Kudekar, Jason K. Johnson, Michael Chertkov
2011 J jnl
Linear Programming based Detectors for Two-Dimensional Intersymbol Interference Channels
CoRR
Shrinivas Kudekar, Jason K. Johnson, Michael Chertkov
2011 B conf
Linear programming based detectors for two-dimensional intersymbol interference channels.
ISIT
Shrinivas Kudekar, Jason K. Johnson, Michael Chertkov
2010 J jnl
A Majorization-Minimization Approach to Design of Power Transmission Networks
CoRR
Jason K. Johnson, Michael Chertkov
2010 — conf
A majorization-minimization approach to design of power transmission networks.
CDC
Jason K. Johnson, Michael Chertkov
2010 J jnl
Learning Planar Ising Models
CoRR
Jason K. Johnson, Praneeth Netrapalli, Michael Chertkov
2009 J jnl
Fixing Convergence of Gaussian Belief Propagation
CoRR
Jason K. Johnson, Danny Bickson, Danny Dolev
2009 B conf
Fixing convergence of Gaussian belief propagation.
ISIT
Danny Dolev, Danny Bickson, Jason K. Johnson
2009 A* conf
Orbit-product representation and correction of Gaussian belief propagation.
ICML
Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov
2008 J jnl
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields.
IEEE Trans. Image Process.
Jason K. Johnson, Alan S. Willsky
2008 — —
Convex relaxation methods for graphical models: Lagrangian and maximum entropy approaches.
Jason K. Johnson
2008 J jnl
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis.
IEEE Trans. Signal Process.
Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky
2008 J jnl
Low-Rank Variance Approximation in GMRF Models: Single and Multiscale Approaches.
IEEE Trans. Signal Process.
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin Choi, Alan S. Willsky
2007 — conf
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.
NIPS
Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky
2007 — conf
GMRF Variance Approximation using Splicedwavelet Bases.
ICASSP (3)
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
2007 J jnl
Lagrangian Relaxation for MAP Estimation in Graphical Models
CoRR
Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky
2007 A conf
Learning Markov Structure by Maximum Entropy Relaxation.
AISTATS
Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky
2006 — conf
Low-Rank Variance Estimation in Large-Scale Gmrf Models.
ICASSP (3)
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
2006 J jnl
Walk-Sums and Belief Propagation in Gaussian Graphical Models.
J. Mach. Learn. Res.
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
2005 — conf
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation.
NIPS
Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky