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stat.ML2026
Robust Assortment Optimization from Observational Data
Miao Lu, Yuxuan Han, Han Zhong +2
Assortment optimization is a fundamental challenge in modern retail and recommendation systems, where the goal is to select a subset of products that maximizes expected revenue und…
stat.ML2025
Learning an Optimal Assortment Policy under Observational Data
Yuxuan Han, Han Zhong, Miao Lu +2
We study the fundamental problem of offline assortment optimization under the Multinomial Logit (MNL) model, where sellers must determine the optimal subset of the products to offe…
stat.ML2024
Precise Asymptotics and Refined Regret of Variance-Aware UCB
Yingying Fan, Yuxuan Han, Jinchi Lv +2
In this paper, we study the behavior of the Upper Confidence Bound-Variance (UCB-V) algorithm for the Multi-Armed Bandit (MAB) problems, a variant of the canonical Upper Confidence…