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Liquid Ensemble Selection for Continual Learning
Continual learning aims to enable machine learning models to continually learn from a shifting data distribution without forgetting what has already been learned.
Liquid Democracy for Low-Cost Ensemble Pruning
We argue that there is a strong connection between ensemble learning and a delegative voting paradigm -- liquid democracy -- that can be leveraged to reduce ensemble training costs.
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