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A Computational Separation between Private Learning and Online Learning Mark Bun Department of Computer Science Boston University Boston, MA02215 null
Neural Information Processing Systems
Moran (NeurIPS 2019) showed that uniform or highly sample-efficient pure-private learners can be time-efficiently compiled into online learners.
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Optimal Comparator Adaptive Online Learning with Switching Cost
Neural Information Processing Systems
It requires a bounded domain.
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Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Neural Information Processing Systems
However, most existing techniques focus only on overcoming CF and have no mechanism to encourage KT, and thus do not do well in KT. Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge.
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Online Learning with Primary and Secondary Losses
Neural Information Processing Systems
We study the problem of online learning with primary and secondary losses.
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