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- W4221145179 abstract "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 ." @default.
- W4221145179 created "2022-04-03" @default.
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- W4221145179 date "2022-02-01" @default.
- W4221145179 modified "2023-10-17" @default.
- W4221145179 title "Datamodels: Predicting Predictions from Training Data" @default.
- W4221145179 doi "https://doi.org/10.48550/arxiv.2202.00622" @default.
- W4221145179 hasPublicationYear "2022" @default.
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