3 citations · 5 across the 7 of their papers we have counts for
7 papers
CRAMER: Control via Request-Aware Masking for Editing Recommenders
Zhiyuan Julian Su, Naihe Feng, Zhen Luther Qin +1
Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user'…
Aligning Dense Retrievers with LLM Utility via Distillation
Rajinder Sandhu, Di Mu, Cheng Chang +4
Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations. Conversely, utility-based ap…
Conformal Agent Error Attribution
Naihe Feng, Yi Sui, Shiyi Hou +2
When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundame…
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
Hongchen Li, Tianyu Li, Jiazhi Yang +10
Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enha…
Self-Supervised Representation Learning as Mutual Information Maximization
Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu +2
Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works att…
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Naihe Feng, Yi Sui, Shiyi Hou +2
Existing research on Retrieval-Augmented Generation (RAG) primarily focuses on improving overall question-answering accuracy, often overlooking the quality of sub-claims within gen…
Self-supervised Representation Learning From Random Data Projectors
Yi Sui, Tongzi Wu, Jesse C. Cresswell +5
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. Whil…