arXiv AI
Jun 2

MINTS: Minimalist Thompson Sampling

arXiv:2606. 01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints.

By Kaizheng Wang
arXiv Machine Learning
Jun 2

Local Preferential Bayesian Optimization

arXiv:2606. 02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function.

By Johanna Menn, Miriam Kober, Paul Brunzema, David Stenger, Sebastian Trimpe
arXiv Machine Learning
6d ago

Diffusion-Based Data-Driven Assortment Optimization

arXiv:2608. 11419v1 Announce Type: new Abstract: Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants.

By Junyi Liao, Xiaohui Jiang, Zhengwei Tong, Ethan X. Fang, Vahid Tarokh