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Jorge Silva

  1. Near-minimax recursive density estimation on the binary hypercube.

    Чт, 12/08/2011 - 15:01 — SomNambul
    Authors: Maxim Raginsky, Rebecca Willett, Jorge Silva, Svetlana Lazebnik
    Subjects: Statistics
    Abstract

    This paper describes a recursive estimation procedure for multivariate binary
    densities (probability distributions of vectors of Bernoulli random variables)
    using orthogonal expansions. For $d$ covariates, there are $2^d$ basis
    coefficients to estimate, which renders conventional approaches computationally
    prohibitive when $d$ is large.

  2. Sequential anomaly detection in the presence of noise and limited feedback.

    Втр, 11/17/2009 - 16:01 — SomNambul
    Authors: Maxim Raginsky, Rebecca Willett, Roummel Marcia, Jorge Silva
    Subjects: Learning
    Abstract

    This paper describes a method for detecting anomalies from sequentially
    observed and potentially noisy data. The proposed approach consists of two main
    elements: (1) filtering, or assigning a belief or likelihood to each successive
    measurement based upon our ability to predict it from previous noisy
    observations, and (2) hedging, or flagging potential anomalies by comparing the
    current belief against a time-varying and data-adaptive threshold. The
    threshold is adjusted based on the available feedback from an end user.

Math-arch

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