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On the Minimax Regret in Online Ranking with Top-k Feedback
April 15, 2024, 4:43 a.m. | Mingyuan Zhang, Ambuj Tewari
cs.LG updates on arXiv.org arxiv.org
Abstract: In online ranking, a learning algorithm sequentially ranks a set of items and receives feedback on its ranking in the form of relevance scores. Since obtaining relevance scores typically involves human annotation, it is of great interest to consider a partial feedback setting where feedback is restricted to the top-$k$ items in the rankings. Chaudhuri and Tewari [2017] developed a framework to analyze online ranking algorithms with top $k$ feedback. A key element in their …
abstract algorithm annotation arxiv cs.lg feedback form human minimax ranking set stat.ml type
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