arXiv Machine Learning By Melveena Jolly, Midhun Xavier

Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective

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arXiv:2607. 11146v1 Announce Type: new Abstract: We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding.

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arXiv Machine Learning
Jul 14

Diversified Multinomial Logit Contextual Bandits

arXiv:2607. 11684v1 Announce Type: cross Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities.

By Heesang Ann, Taehyun Hwang, Min-hwan Oh