18 citations · 21 across the 6 of their papers we have counts for
6 papers
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…
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization
Zixin Ding, Si Chen, Ruoxi Jia +1
Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often…
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints
Fengxue Zhang, Zejie Zhu, Yuxin Chen
Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific exper…
Learning Human-Compatible Representations for Case-Based Decision Support
Han Liu, Yizhou Tian, Chacha Chen +3
Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of…
A Contract Theory based Incentive Mechanism for Federated Learning
Mengmeng Tian, Yuxin Chen, Yuan Liu +3
Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task,…
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Qixing Huang, Yuxin Chen, Leonidas Guibas
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for…