Feb. 9, 2024, 5:44 a.m. | Joohwan Ko Andrew A. Li

cs.LG updates on arXiv.org arxiv.org

Models of choice are a fundamental input to many now-canonical optimization problems in the field of Operations Management, including assortment, inventory, and price optimization. Naturally, accurate estimation of these models from data is a critical step in the application of these optimization problems in practice. Concurrently, recent advancements in deep learning have sparked interest in integrating these techniques into choice modeling. However, there is a noticeable research gap at the intersection of deep learning and choice modeling, particularly with both …

application attention canonical cs.ai cs.lg data deep learning inventory management modeling operations optimization practice price price optimization self-attention via

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Codec Avatars Research Engineer

@ Meta | Pittsburgh, PA