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Creators/Authors contains: "Sanz-Alonso, D"

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  1. ; (, Information and inference)
    Accepted Manuscript Full Text Available
  2. ; ; (, Foundations of data science)
    Full Text Available 
  3. ; ; (, Notices of the American Mathematical Society)
    Full Text Available 
  4. ; (, SIAMASA journal on uncertainty quantification)
  5. ; ; (, Notices of the American Mathematical Society)
    Full Text Available 
  6. ; ; (, Notices of the American Mathematical Society)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, SIAMASA journal on uncertainty quantification)
  8. ; ; (, Journal of Computational Physics)
    null (Ed.)
    Full Text Available 
  9. ; ; ; ; (, Inverse Problems)
    null (Ed.)
    Full Text Available 
  10. ; ; ; (, Journal of machine learning research)
    This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory. 
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    Accepted Manuscript Full Text Available