Jason K. Johnson

According to our database, Jason K. Johnson authored at least 18 papers between 2005 and 2016.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2016

Learning Planar Ising Models.

Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli

J. Mach. Learn. Res., 2016

2013

Improved linear programming decoding using frustrated cycles.

Shrinivas Kudekar, Jason K. Johnson, Misha Chertkov

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

2011

Linear programming based detectors for two-dimensional intersymbol interference channels.

Shrinivas Kudekar, Jason K. Johnson, Michael Chertkov

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

2010

Learning Planar Ising Models

Jason K. Johnson, Praneeth Netrapalli, Michael Chertkov

CoRR, 2010

A majorization-minimization approach to design of power transmission networks.

Jason K. Johnson, Michael Chertkov

Proceedings of the 49th IEEE Conference on Decision and Control, 2010

2009

Fixing convergence of Gaussian belief propagation.

Danny Dolev, Danny Bickson, Jason K. Johnson

Proceedings of the IEEE International Symposium on Information Theory, 2009

Orbit-product representation and correction of Gaussian belief propagation.

Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Convex relaxation methods for graphical models: Lagrangian and maximum entropy approaches.

Jason K. Johnson

PhD thesis, 2008

Low-Rank Variance Approximation in GMRF Models: Single and Multiscale Approaches.

Dmitry M. Malioutov, Jason K. Johnson, Myung Jin Choi, Alan S. Willsky

IEEE Trans. Signal Process., 2008

Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis.

Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky

IEEE Trans. Signal Process., 2008

A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields.

Jason K. Johnson, Alan S. Willsky

IEEE Trans. Image Process., 2008

2007

Learning Markov Structure by Maximum Entropy Relaxation.

Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Lagrangian Relaxation for MAP Estimation in Graphical Models

Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky

CoRR, 2007

Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.

Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

GMRF Variance Approximation using Splicedwavelet Bases.

Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky

Proceedings of the IEEE International Conference on Acoustics, 2007

2006

Walk-Sums and Belief Propagation in Gaussian Graphical Models.

Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky

J. Mach. Learn. Res., 2006

Low-Rank Variance Estimation in Large-Scale Gmrf Models.

Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky

Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006

2005

Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation.

Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005