A new impossibility theorem shows that adaptive multitask learning fails even with arbitrarily large per-task sample sizes, provided the number of tasks is at least n^{nβ/(1−β)}.
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When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
A new impossibility theorem shows that adaptive multitask learning fails even with arbitrarily large per-task sample sizes, provided the number of tasks is at least n^{nβ/(1−β)}.