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Found 5 papers, 1 papers with code
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets.
CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection
Another key contribution is the study of data valuation in the domain adaptation setting, where a data value estimator obtained using checkpoints from training trajectory in the source domain training dataset is used for data valuation in a target domain training dataset.
Finding High-Value Training Data Subset through Differentiable Convex Programming
In this paper, we study the problem of selecting high-value subsets of training data.
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving
We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables.
TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving
Training vision-based Autonomous driving models is a challenging problem with enormous practical implications.
