1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning
Ningxin He, Yang Liu, Wei Sun +4
Text-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the avail…
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
Chengtao Jian, Kai Yang, Tianhao Gao +5
Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise pref…
LLM-Explorer: Towards Efficient and Affordable LLM-based Exploration for Mobile Apps
Shanhui Zhao, Hao Wen, Wenjie Du +5
Large language models (LLMs) have opened new opportunities for automated mobile app exploration, an important and challenging problem that used to suffer from the difficulty of gen…
Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
Tianyuan Zou, Yang Liu, Peng Li +6
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that res…