3 papers
cs.SI2025
Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation
Difu Feng, Qianqian Xu, Zitai Wang +3
Nowadays, recommendation systems have become crucial to online platforms, shaping user exposure by accurate preference modeling. However, such an exposure strategy can also reinfor…
cs.LG2025
TuckA: Hierarchical Compact Tensor Experts for Efficient Fine-Tuning
Qifeng Lei, Zhiyong Yang, Qianqian Xu +3
Efficiently fine-tuning pre-trained models for downstream tasks is a key challenge in the era of foundation models. Parameter-efficient fine-tuning (PEFT) presents a promising solu…
cs.LG2025
OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning
Cong Hua, Qianqian Xu, Zhiyong Yang +3
Prompt tuning adapts Vision-Language Models like CLIP to open-world tasks with minimal training costs. In this direction, one typical paradigm evaluates model performance separatel…