Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Datamodels: Predicting Predictions from Training Data   We present a conceptual framework, datamodeling, for analyzing the behavior
of a model class in terms of the training data. For any fixed "target" example
$x$, training set $S$, and learning algorithm, a datamodel is a parameterized
function $2^S \to \mathbb{R}$ that for any subset of $S' \subset S$ -- using
only information about which examples of $S$ are contained in $S'$ -- predicts
the outcome of training a model on $S'$ and evaluating on $x$. Despite the
potential complexity of the underlying process being approximated (e.g.,
end-to-end training and evaluation of deep neural networks), we show that even
simple linear datamodels can successfully predict model outputs. We then
demonstrate that datamodels give rise to a variety of applications, such as:
accurately predicting the effect of dataset counterfactuals; identifying
brittle predictions; finding semantically similar examples; quantifying
train-test leakage; and embedding data into a well-behaved and feature-rich
representation space. Data for this paper (including pre-computed datamodels as
well as raw predictions from four million trained deep neural networks) is
available at https://github.com/MadryLab/datamodels-data .

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: econ