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Found 2 papers, 1 papers with code
AutoML in Heavily Constrained Applications
In this paper, we propose CAML, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand.
ED2: Two-stage Active Learning for Error Detection -- Technical Report
The challenges for such an approach are twofold: (1) to represent the data in a way that enables a classification model to identify various kinds of data errors, and (2) to pick the most promising data values for learning.
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