Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Imputation and Stochastic Prediction   In many machine learning applications, we are faced with incomplete datasets.
In the literature, missing data imputation techniques have been mostly
concerned with filling missing values. However, the existence of missing values
is synonymous with uncertainties not only over the distribution of missing
values but also over target class assignments that require careful
consideration. In this paper, we propose a simple and effective method for
imputing missing features and estimating the distribution of target assignments
given incomplete data. In order to make imputations, we train a simple and
effective generator network to generate imputations that a discriminator
network is tasked to distinguish. Following this, a predictor network is
trained using the imputed samples from the generator network to capture the
classification uncertainties and make predictions accordingly. The proposed
method is evaluated on CIFAR-10 and MNIST image datasets as well as five
real-world tabular classification datasets, under different missingness rates
and structures. Our experimental results show the effectiveness of the proposed
method in generating imputations as well as providing estimates for the class
uncertainties in a classification task when faced with missing values.

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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
2
The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn