We introduced and analyzed two novel gossip algorithms for rank and trimmed means estimation, proving convergence rates of $\mathcal{O}(1/t)$.

We decompose the gap between selective classifiers and the ideal oracle into five measurable sources, showing that only non-monotone scoring methods can reduce it and improve reliability.