Corpus ID: 195345228

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

@article{Grover2019BiasCO,
  title={Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting},
  author={Aditya Grover and Jiaming Song and A. Agarwal and K. Tran and A. Kapoor and E. Horvitz and S. Ermon},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.09531}
}
  • Aditya Grover, Jiaming Song, +4 authors S. Ermon
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • A learned generative model often produces biased statistics relative to the underlying data distribution. [...] Key Result Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data.Expand Abstract
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 72 REFERENCES
    Generative Adversarial Nets
    • 17,776
    • PDF
    Auto-Encoding Variational Bayes
    • 9,147
    • PDF
    On sequential Monte Carlo sampling methods for Bayesian filtering
    • 4,378
    • PDF
    Improved Techniques for Training GANs
    • 3,585
    • PDF
    Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
    • 2,222
    • PDF
    Automatic differentiation in PyTorch
    • 6,546
    An Introduction to the Bootstrap
    • 9,934
    • PDF
    GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
    • 1,748
    • PDF