2 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
Multi-Objective Bilevel Learning
Zhiyao Zhang, Zhuqing Liu, Xin Zhang +3
As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled…
Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows
Xin Zhang, Weiliang Li, Rui Li +9
Optimizing conversions is crucial in modern online advertising systems, enabling advertisers to deliver relevant products to users and drive business outcomes. However, accurately…
CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance
Wei Wen, Quanyu Zhu, Weiwei Chu +2
Scaling up deep learning models has been proven effective to improve intelligence of machine learning (ML) models, especially for industry recommendation models and large language…
SiGeo: Sub-One-Shot NAS via Information Theory and Geometry of Loss Landscape
Hua Zheng, Kuang-Hung Liu, Igor Fedorov +3
Neural Architecture Search (NAS) has become a widely used tool for automating neural network design. While one-shot NAS methods have successfully reduced computational requirements…
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…