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Creators/Authors contains: "Domke, Justin"

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  1. ; ; (, Association for Uncertainty in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  2. ; ; (, The 40th Conference on Uncertainty in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  3. ; ; (, International Conference on Artifi- cial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  4. ; (, Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  5. ; ; (, Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  6. ; ; (, Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Transactions on machine learning research)
    We propose the use of U-statistics to reduce variance for gradient estimation in importance-weighted variational inference. The key observation is that, given a base gradient estimator that requires m > 1 samples and a total of n > m samples to be used for estimation, lower variance is achieved by averaging the base estimator on overlapping batches of size m than disjoint batches, as currently done. We use classical U-statistic theory to analyze the variance reduction, and propose novel approximations with theoretical guarantees to ensure computational efficiency. We find empirically that U-statistic variance reduction can lead to modest to significant improvements in inference performance on a range of models, with little computational cost. 
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    Accepted Manuscript Full Text Available
  8. ; (, The international conference on machine learning)
    Accepted Manuscript Full Text Available
  9. ; ; (, artificial intelligence and statistics)
    Accepted Manuscript Full Text Available
  10. ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available