How much data is sufficient to learn high-performing algorithms?

8 Aug 2019Maria-Florina BalcanDan DeBlasioTravis DickCarl KingsfordTuomas SandholmEllen Vitercik

Algorithms -- for example for scientific analysis -- typically have tunable parameters that significantly influence computational efficiency and solution quality. If a parameter setting leads to strong algorithmic performance on average over a set of training instances, that parameter setting -- ideally -- will perform well on previously unseen future instances... (read more)

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