6 citations · 7 across the 8 of their papers we have counts for
8 papers
IRIS: Implicit Reward-Guided Internal Sifting for Mitigating Multimodal Hallucination
Yuanshuai Li, Yuping Yan, Jirui Han +3
Hallucination remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While Direct Preference Optimization (DPO) is a key alignment framework, existing approa…
TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data
Yuping Yan, Yizhi Wang, Yuanshuai Li +1
Serial pipeline training is an efficient paradigm for handling data heterogeneity in cross-silo federated learning with low communication overhead. However, even without centralize…
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems
Xinan Chen, Rong Qu, Jing Dong +2
Dynamic scheduling in real-world environments often struggles to adapt to unforeseen disruptions, making traditional static scheduling methods and human-designed heuristics inadequ…
Guiding Multi-agent Multi-task Reinforcement Learning by a Hierarchical Framework with Logical Reward Shaping
Chanjuan Liu, Jinmiao Cong, Bingcai Chen +2
Multi-agent hierarchical reinforcement learning (MAHRL) has been studied as an effective means to solve intelligent decision problems in complex and large-scale environments. Howev…
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal Systems
Guodong Chen, Jiu Jimmy Jiao, Qiqi Liu +2
Multi-objective optimization has burgeoned as a potent methodology for informed decision-making in enhanced geothermal systems, aiming to concurrently maximize economic yield, ensu…
FedSlate:A Federated Deep Reinforcement Learning Recommender System
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +1
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems…
Privacy-preserving Quantification of Non-IID Degree in Federated Learning
Yuping Yan, Yizhi Wang, Yingchao Yu +1
Federated learning (FL) offers a privacy-preserving approach to machine learning for multiple collaborators without sharing raw data. However, the existence of non-independent and…
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
Yingchao Yu, Yuping Yan, Jisong Cai +1
Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume tha…