Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Finite element solution of boundary value problems: theory and computation
Finite element solution of boundary value problems: theory and computation
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Linear Optimization
Introduction to Linear Optimization
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient inference with cardinality-based clique potentials
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
MAP-Inference for Highly-Connected Graphs with DC-Programming
Proceedings of the 30th DAGM symposium on Pattern Recognition
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Iterated conditional modes for inverse dithering
Signal Processing
Improving quality of training data for learning to rank using click-through data
Proceedings of the third ACM international conference on Web search and data mining
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
The Journal of Machine Learning Research
Mean squared residue based biclustering algorithms
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
MAP estimation, message passing, and perfect graphs
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Unsupervised Learning for Graph Matching
International Journal of Computer Vision
Closed-Form relaxation for MRF-MAP tissue classification using discrete laplace equations
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
| Hi-index | 0.00 |
Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem. An additional convex relaxation of the quadratic approximation is shown to have additive approximation guarantees that apply even when the graph weights have mixed sign or do not come from a metric. The approximations are extended in a manner that allows tight variational relaxations of the MAP problem, although they generally involve non-convex optimization. Experiments carried out on synthetic data show that the quadratic approximations can be more accurate and computationally efficient than the linear programming and propagation based alternatives.