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Coresets are small data summaries that are sufficient for model training.They can be maintained online, enabling efficient handling of large datastreams under resource constraints. However, existing constructions are limitedto simple models such as k-means and logistic regression. In this work, wepropose a novel coreset construction via cardinality-constrained bileveloptimization. We show how our framework can efficiently generate coresets fordeep neural networks, and demonstrate its empirical benefits in continuallearning and in streaming settings.

Coresets via Bilevel Optimization for Continual Learning and Streaming