From the 1 of 7 linked papers with an AI index.
7 papers
AutoSIFT: Automatic Style Sifting for Controllable Speech Generation with Arbitrary Style Infilling
Haowei Lou, Junda Wu, Chengkai Huang +4
AutoSIFT is a text-to-speech framework that separates speaking style into explicit categories (e.g., emotion, age) and residual prosodic details, allowing users to edit specific st…
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Hongtao Huang, Chengkai Huang, Junda Wu +3
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…
Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learning
Xiaocong Chen, Siyu Wang, Tong Yu +1
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. A central concern in this context is ensuring the safety of the learned…
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…