Corpus ID: 220623774

Reflection methods for user-friendly submodular optimization

@inproceedings{Jegelka2013ReflectionMF,
  title={Reflection methods for user-friendly submodular optimization},
  author={S. Jegelka and Francis R. Bach and S. Sra},
  booktitle={NIPS},
  year={2013}
}
  • S. Jegelka, Francis R. Bach, S. Sra
  • Published in NIPS 2013
  • Computer Science, Mathematics
  • Recently, it has become evident that submodularity naturally captures widely occurring concepts in machine learning, signal processing and computer vision. Consequently, there is need for efficient optimization procedures for submodular functions, especially for minimization problems. While general submodular minimization is challenging, we propose a new method that exploits existing decomposability of submodular functions. In contrast to previous approaches, our method is neither approximate… CONTINUE READING
    64 Citations
    Convex Optimization for Parallel Energy Minimization
    • 8
    • PDF
    Active-set Methods for Submodular Minimization Problems
    • 5
    • Highly Influenced
    • PDF
    Provable Submodular Minimization using Wolfe's Algorithm
    • 44
    • PDF
    Learning with Submodular Functions: A Convex Optimization Perspective
    • 325
    • PDF
    Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
    • I. M OTIVATION
    • 2018
    • 163
    • PDF
    Quadratic Decomposable Submodular Function Minimization: Theory and Practice
    • Highly Influenced
    • PDF
    Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems
    • 76
    • PDF
    Min Norm Point Algorithm for Higher Order MRF-MAP Inference
    • 10
    • PDF
    Quadratic Decomposable Submodular Function Minimization: Theory and Practice
    • 6
    • Highly Influenced
    • PDF
    From MAP to Marginals: Variational Inference in Bayesian Submodular Models
    • 66
    • PDF

    References

    SHOWING 1-10 OF 67 REFERENCES
    MRF Energy Minimization and Beyond via Dual Decomposition
    • 316
    • Highly Influential
    • PDF
    Learning with Submodular Functions: A Convex Optimization Perspective
    • 325
    • PDF
    Efficient Minimization of Decomposable Submodular Functions
    • 116
    • Highly Influential
    • PDF
    A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling
    • 63
    • Highly Influential
    • PDF
    Proximal Methods for Hierarchical Sparse Coding
    • 313
    • PDF
    Proximal Splitting Methods in Signal Processing
    • P. L. Combettes, J. Pesquet
    • Computer Science, Mathematics
    • Fixed-Point Algorithms for Inverse Problems in Science and Engineering
    • 2011
    • 2,052
    • Highly Influential
    • PDF
    Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
    • 30
    • PDF
    Fast Newton-type Methods for Total Variation Regularization
    • 78
    • PDF