Contrastive Neural Ratio Estimation

@article{Miller2022ContrastiveNR,
  title={Contrastive Neural Ratio Estimation},
  author={Benjamin Kurt Miller and Christoph Weniger and Patrick Forr'e},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.06170}
}
Likelihood-to-evidence ratio estimation is usually cast as either a binary ( NRE - A ) or a multiclass ( NRE - B ) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliable. We propose a multiclass framework free from the bias inherent to NRE - B at optimum, leaving us in the position to run diagnostics that practitioners depend on. It also… 

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