Research map / Architectures and efficiency / Pruning and efficient tuning
Balancing Act: Constraining Disparate Impact in Sparse Models
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Proposes constraining disparate impact of pruning on data sub-groups in sparse models, using the abstract and an ICLR template as the only available content.
Problem
Pruned sparse models achieve near-dense overall accuracy but suffer high accuracy drops on some data sub-groups, a disparate impact induced by pruning.
Approach
Abstract is truncated; the method is only partially described as addressing disparate impact in sparse models by constraining it during pruning. Details beyond the abstract are not available in the provided text.
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