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  1. Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix

    Publication
    Given an intractable target density p, variational inference (VI) attempts to find the best approximation q from a tractable family Q. This is typically done by minimizing the …

  2. Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

    Publication
    Given an intractable distribution p, the problem of variational inference (VI) is to find the best approximation from some more tractable family Q. Commonly, one chooses Q to …

  3. Variational Inference with Gaussian Score Matching

    Publication
    Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics. Typically, VI fits a simple parametric distribution to be close …

  4. The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference

    Publication
    When factorized approximations are used for variational inference (VI), they tend to underestimate the uncertainty -- as measured in various ways -- of the distributions they are meant to approximate …

  5. A geometrical connection between sparse and low-rank matrices and its application to manifold learning

    Publication
    We consider when a sparse nonnegative matrix (mathbf{S}) can be recovered, via an elementwise nonlinearity, from a real-valued matrix~ (mathbf{S}) of significantly lower rank. Of …