Introduces P2MLE-UCB and GP2-UCB round-based algorithms achieving optimal regret bounds for multiplicative and general position-aware MNL bandits, with efficient optimization subroutines.
arXiv preprint arXiv:2502.06777 , year=
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2026 3representative citing papers
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.
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Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
Introduces P2MLE-UCB and GP2-UCB round-based algorithms achieving optimal regret bounds for multiplicative and general position-aware MNL bandits, with efficient optimization subroutines.
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Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.
- Optimal Exploration of New Products under Assortment Decisions