On the Learnability of Distribution Classes with Adaptive Adversaries
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Abstract (arXiv)
We consider the question of learnability of distribution classes in the presence of adaptive adversaries -- that is, adversaries capable of intercepting the samples requested by a learner and applying manipulations with full knowledge of the samples before passing it on to the learner. This stands in contrast to oblivious adversaries, who can only modify the underlying distribution the samples come from but not their i.i.d. nature. We formulate a general notion of learnability with respect to adaptive adversaries, taking into account the budget of the adversary. We show that learnability with respect to additive adaptive adversaries is a strictly stronger condition than learnability with respect to additive oblivious adversaries.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 5 Sep 2025.
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19 of 38 references.
References · 19
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7 more
- 2021Learning GMMs with Nearly Optimal Robustness Guaranteescited by 12
- 2021On the power of adaptivity in statistical adversariescited by 12
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Citing papers per year: 2026: 1.
1 paper.