2 citations · 4 across the 5 of their papers we have counts for
6 papers
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
Shaohuai Shi, Qing Yang, Yang Xiang +2
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for…
Listening to Users' Voice: Automatic Summarization of Helpful App Reviews
Cuiyun Gao, Yaoxian Li, Shuhan Qi +4
App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features…
Parameter-Efficient and Student-Friendly Knowledge Distillation
Jun Rao, Xv Meng, Liang Ding +2
Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixe…
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Xiaohan Hou +3
Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor s…
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Huale Li, Xuan Wang, Fengwei Jia +4
Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies…
Solving imperfect-information games via exponential counterfactual regret minimization
Huale Li, Xuan Wang, Shuhan Qi +4
In general, two-agent decision-making problems can be modeled as a two-player game, and a typical solution is to find a Nash equilibrium in such game. Counterfactual regret minimiz…