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Targeted sampling of informative pairs can slash computational costs in pairwise loss estimation while maintaining performance on par with full evaluations.
Achieve reliable ranking consensus in decentralized systems with provable convergence guarantees using gossip algorithms for Borda and Copeland rules.
