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Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
We develop and analyze M-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a nonasymptotic variational characterizationExpand
Convergence of latent mixing measures in finite and infinite mixture models
This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i.e., Wasserstein metrics). The relationshipExpand
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis
TLDR
This paper presents theorems elucidating the posterior contraction rates of the topics as the amount of data increases, and a thorough supporting empirical study using synthetic and real data sets, including news and web-based articles and tweet messages. Expand
A kernel-based learning approach to ad hoc sensor network localization
TLDR
It is shown 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, and a simple and effective localization algorithm is derived. Expand
Reviving Partial Order Planning
This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitiveExpand
Nonparametric decentralized detection using kernel methods
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 distributionExpand
In-Network PCA and Anomaly Detection
TLDR
A PCA-based anomaly detector in which adaptive local data filters send to a coordinator just enough data to enable accurate global detection is developed, based on a stochastic matrix perturbation analysis that characterizes the tradeoff between the accuracy of anomaly detection and the amount of data communicated over the network. Expand
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
TLDR
An algorithm for nonparametric estimation of divergence functionals and the density ratio of two probability distributions is developed and analyzed, based on a variational characterization of f-divergences, which turns the estimation into a penalized convex risk minimization problem. Expand
ON surrogate loss functions and f-divergences
TLDR
This work considers an elaboration of binary classification in which the covariates are not available directly but are transformed by a dimensionality-reducing quantizer Q, and makes it possible to pick out the (strict) subset of surrogate loss functions that yield Bayes consistency for joint estimation of the discriminant function and the quantizer. Expand
Communication-Efficient Online Detection of Network-Wide Anomalies
TLDR
This work proposes a novel approximation scheme that dramatically reduces the burden on the production network of a PCA-based anomaly detection scheme and selects the filtering parameters at local monitors such that the errors made by the detector are guaranteed to lie below a user-specified upper bound. Expand
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