Tootfinder

Fairness-Aware Static and Dynamic Assortment Optimization: Optimal Selection with Balanced Market Share
Omar El Housni, Qing Feng, Huseyin Topaloglu
https://arxiv.org/abs/2507.05606

Fairness-Aware Static and Dynamic Assortment Optimization: Optimal Selection with Balanced Market Share
Assortment optimization is a critical tool for online retailers aiming to maximize revenue. However, optimizing purely for revenue can lead to imbalanced sales across products, potentially causing supplier disengagement and reduced product diversity. To address these fairness concerns, we introduce a market share balancing constraint that limits the disparity in expected sales between any two offered products to a factor of a given parameter $α$. We study both static and dynamic assortment opt…

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Kazuto Fukuchi
https://arxiv.org/abs/2506.13947 https://

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algori…