Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Decentralized learning with budgeted network load using Gaussian copulas
  and classifier ensembles   We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELCO

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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: eess