1 citations · 1 across the 8 of their papers we have counts for
8 papers
GSAR: Goal-State-Anchor Rewards for Mobile GUI Agents with Self-Evolving Data Synthesis
Long Zhang, Yuhan Chen, Chaoran Zhang +7
Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental is…
Generative Auto-Bidding with Unified Modeling and Exploration
Mingming Zhang, Feiqing Zhuang, Na Li +7
Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Ma…
Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
Mingming Zhang, Na Li, Zhuang Feiqing +8
With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches fo…
LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Zhiyong Xie, Lixin Zou +7
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information up…
Multi-Interest Recommendation: A Survey
Zihao Li, Qiang Chen, Lixin Zou +2
Existing recommendation methods often struggle to model users' multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty an…
Flow Matching based Sequential Recommender Model
Feng Liu, Lixin Zou, Xiangyu Zhao +5
Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due…
Efficient Sparse Attention needs Adaptive Token Release
Chaoran Zhang, Lixin Zou, Dan Luo +4
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their `large' scale introduces significa…
Unconfounded Propensity Estimation for Unbiased Ranking
Dan Luo, Lixin Zou, Qingyao Ai +4
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms that…