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