High Dimensional Inference with Random Maximum A-Posteriori Perturbations

@article{Hazan2016HighDI,
  title={High Dimensional Inference with Random Maximum A-Posteriori Perturbations},
  author={Tamir Hazan and Francesco Orabona and Anand D. Sarwate and Subhransu Maji and Tommi S. Jaakkola},
  journal={CoRR},
  year={2016},
  volume={abs/1602.03571}
}
In this work we present a new approach for high-dimensional statistical inference that is based on optimization and random perturbations. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing its potential function. When the perturbations are of low dimension, sampling the perturb-max prediction is as efficient as MAP optimization. A classic result from extreme value statistics asserts that perturb-max operations generate unbiased samples from the… CONTINUE READING

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