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M-decomposability, elliptical unimodal densities, and applications to clustering and kernel density estimation
Chia and Nakano (2009) introduced the concept of M-decomposability of pr...
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A One-Class Decision Tree Based on Kernel Density Estimation
One-Class Classification (OCC) is a domain of machine learning which ach...
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Some techniques in density estimation
Density estimation is an interdisciplinary topic at the intersection of ...
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Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities
It is well-known that density estimation on the unit interval is asympto...
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Obfuscation via Information Density Estimation
Identifying features that leak information about sensitive attributes is...
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Algorithms and Theory for Multiple-Source Adaptation
This work includes a number of novel contributions for the multiple-sour...
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Iterative Multilevel density estimation for McKean-Vlasov SDEs via projections
In this paper, we present a generic methodology for the efficient numeri...
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Multiple-Source Adaptation with Domain Classifiers
We consider the multiple-source adaptation (MSA) problem and improve a previously proposed MSA solution, where accurate density estimation per domain is required to obtain favorable learning guarantees. In this work, we replace the difficult task of density estimation per domain with a much easier task of domain classification, and show that the two solutions are equivalent given the true densities and domain classifier, yet the newer approach benefits from more favorable guarantees when densities and domain classifier are estimated from finite samples. Our experiments with real-world applications demonstrate that the new discriminative MSA solution outperforms the previous solution with density estimation, as well as other domain adaptation baselines.
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