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Poster
Coresets via Bilevel Optimization for Continual Learning and Streaming
Zalán Borsos · Mojmir Mutny · Andreas Krause

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1082

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.

Author Information

Zalán Borsos (ETH Zurich)
Mojmir Mutny (ETH Zurich)
Andreas Krause (ETH Zurich)

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