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This paper addresses the fixed-confidence top-k identification problem from noisy pairwise comparisons, and develops an asymptotically optimal algorithm that minimizes the expected number of comparisons.
This paper critiques the use of pairwise comparisons for learning human preferences, arguing that internal pluralism (multiple conflicting priorities) undermines the standard approach. It proposes a formal model and suggests that allowing indecision can improve learning efficiency.