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Mean estimation is possible even when an adversary shifts a fraction of your data, provided the base distribution's characteristic function meets certain spectral conditions.
Forget faster algorithms: learning halfspaces agnostically under smoothed distributions hits a fundamental complexity wall at $d^{Ω(1/σ^{2}+\log(1/ε))}$, suggesting current upper bounds are nearly tight.
