23 citations · 35 across the 11 of their papers we have counts for
11 papers
MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
Yuantong Li, Lei Yuan, Zhihao Zheng +27
Modern recommendation systems rank candidates by aggregating multiple behavioral signals through a value model. However, many commonly used signals are inherently affected by heter…
HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback
Xufeng Cai, Ziwei Guan, Lei Yuan +11
Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate…
Model Generation with Provable Coverability for Offline Reinforcement Learning
Chengxing Jia, Hao Yin, Chenxiao Gao +4
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could ad…
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
Yi Guo, Zhaocheng Liu, Jianchao Tan +6
In industry, feature selection is a standard but necessary step to search for an optimal set of informative feature fields for efficient and effective training of deep Click-Throug…
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors
Renyu Zhu, Lei Yuan, Xiang Li +2
Program understanding is a fundamental task in program language processing. Despite the success, existing works fail to take human behaviors as reference in understanding programs.…
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Xiangru Lian, Binhang Yuan, Xuefeng Zhu +24
Deep learning based models have dominated the current landscape of production recommender systems. Furthermore, recent years have witnessed an exponential growth of the model scale…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Daochen Zha, Wenye Ma, Lei Yuan +2
Exploration under sparse reward is a long-standing challenge of model-free reinforcement learning. The state-of-the-art methods address this challenge by introducing intrinsic rewa…
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
Yunjie Zhang, Fei Tao, Xudong Liu +6
With the rising of short video apps, such as TikTok, Snapchat and Kwai, advertisement in short-term user-generated videos (UGVs) has become a trending form of advertising. Predicti…
Themes Informed Audio-visual Correspondence Learning
Runze Su, Fei Tao, Xudong Liu +6
The applications of short-term user-generated video (UGV), such as Snapchat, and Youtube short-term videos, booms recently, raising lots of multimodal machine learning tasks. Among…
: Decentralization Meets Error-Compensated Compression
Hanlin Tang, Xiangru Lian, Shuang Qiu +4
Communication is a key bottleneck in distributed training. Recently, an \emph{error-compensated} compression technology was particularly designed for the \emph{centralized} learnin…