Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization.

Alireza Salemi|Hamed Zamani


Anthology ID:DBLP:conf/ictir/SalemiZ25
Volume:Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025
Year:2025
Venue:International Conference on the Theory of Information Retrieval (ICTIR)
Publisher:ACM
Pages:183-193
URL:https://doi.org/10.1145/3731120.3744584
DOI:https://doi.org/10.1145/3731120.3744584
DBLP:conf/ictir/SalemiZ25
BibTeX:
@inproceedings{salemi-2025-learning, author = {Alireza Salemi and Hamed Zamani}, editor = {Hamed Zamani and Laura Dietz and Benjamin Piwowarski and Sebastian Bruch}, title = {{Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization}}, booktitle = {{Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025}}, pages = {183--193}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3731120.3744584}, doi = {https://doi.org/10.1145/3731120.3744584} }