XuanLong Nguyen

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We develop and analyze <i>M</i>-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a nonasymptotic variational characterization of <i>f</i> -divergences, which allows the problem of estimating divergences to be tackled via convex empirical risk optimization. The resulting(More)
We show that the coarse-grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. This stems from an observation that the kernel function, which is a similarity measure critical to the effectiveness of a kernel-based learning(More)
This paper challenges the prevailing pessimism about the scale-up ability of partial order planners (POP) by presenting several novel heuristic control techniques that make the PO planners competitive with the state of the art plan synthesis algorithms. Our critical insight is that the techniques responsible for the efficiency of the currently successful(More)
Most recent strides in scaling up planning have centered around two competing themes–disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r. In this paper, we present a novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are(More)
We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. In contrast to most previous work, in which the joint distribution of sensor observations is assumed to be known, we address the problem when only a set of empirical samples is available. We propose a novel algorithm using the(More)
Topic models such as the latent Dirichlet allocation (LDA) have become a standard staple in the modeling toolbox of machine learning. They have been applied to a vast variety of data sets, contexts, and tasks to varying degrees of success. However, to date there is almost no formal theory explicating the LDA’s behavior, and despite its familiarity there is(More)
There has been growing interest in building large-scale distributed monitoring systems for sensor, enterprise, and ISP networks. Recent work has proposed using principal component analysis (PCA) over global traffic matrix statistics to effectively isolate network-wide anomalies. To allow such a PCA-based anomaly detection scheme to scale, we propose a novel(More)
We consider the problem of network anomaly detection in large distributed systems. In this setting, Principal Component Analysis (PCA) has been proposed as a method for discovering anomalies by continuously tracking the projection of the data onto a residual subspace. This method was shown to work well empirically in highly aggregated networks, that is,(More)
We develop and analyze an algorithm for nonparametric estimation of divergence functionals and the density ratio of two probability distributions. Our method is based on a variational characterization of f -divergences, which turns the estimation into a penalized convex risk minimization problem. We present a derivation of our kernel-based estimation(More)
The goal of binary classification is to estimate a discriminant function γ from observations of covariate vectors and corresponding binary labels. We consider an elaboration of this problem in which the covariates are not available directly, but are transformed by a dimensionality-reducing quantizer Q. We present conditions on loss functions such that(More)