Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

@inproceedings{Seeger2009LargeSV,
  title={Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models},
  author={Matthias W. Seeger and Hannes Nickisch},
  booktitle={Sampling-based Optimization in the Presence of Uncertainty},
  year={2009}
}
Sparsity is a fundamental concept of modern statistics, and often the only general principle available at the moment to address novel learning applications with many more variables than observations. While much progress has been made recently in the theoretical understanding and algorithmics of sparse point estimation, higher-order problems such as covariance estimation or optimal data acquisition are seldomly addressed for sparsity-favouring models, and there are virtually no algorithms for… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 49 references

Convex Optimization

  • S. Boyd, L. Vandenberghe
  • 2002
Highly Influential
11 Excerpts

Nonlinear experiments: Optimal design and inference based on likelihood

  • P. Chaudhuri, P. Mykland
  • Journal of the American Statistical Association,
  • 1993
Highly Influential
4 Excerpts

The Lanczos algorithm with selective orthogonalization

  • B. Parlett, D. Scott
  • Mathematics of Computation,
  • 1979
Highly Influential
9 Excerpts

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