3 papers
cs.IR2023
AutoML for Large Capacity Modeling of Meta's Ranking Systems
Hang Yin, Kuang-Hung Liu, Mengying Sun +16
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can release…
cs.LG2023
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
Wei Wen, Kuang-Hung Liu, Igor Fedorov +19
Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are eva…
cs.LG2021
A Validation Tool for Designing Reinforcement Learning Environments
Ruiyang Xu, Zhengxing Chen
Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products. Although research…