49 citations · 105 across the 18 of their papers we have counts for
9 papers · 1 filter
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Xinyuan Ji, Xu Zhang, Wei Xi +3
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more fr…
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
Didi Zhu, Zexi Li, Min Zhang +6
Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the lea…
GFlowNets with Human Feedback
Yinchuan Li, Shuang Luo, Yunfeng Shao +1
We propose the GFlowNets with Human Feedback (GFlowHF) framework to improve the exploration ability when training AI models. For tasks where the reward is unknown, we fit the rewar…
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Yinchuan Li, Zhigang Li, Wenqian Li +3
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neur…
Multi-agent Policy Reciprocity with Theoretical Guarantee
Haozhi Wang, Yinchuan Li, Qing Wang +2
Modern multi-agent reinforcement learning (RL) algorithms hold great potential for solving a variety of real-world problems. However, they do not fully exploit cross-agent knowledg…
Universal Domain Adaptation via Compressive Attention Matching
Didi Zhu, Yincuan Li, Junkun Yuan +3
Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in ho…
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks
Wenqian Li, Yinchuan Li, Zhigang Li +2
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph,…
CFlowNets: Continuous Control with Generative Flow Networks
Yinchuan Li, Shuang Luo, Haozhi Wang +1
Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate dis…
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
Xu Zhang, Wenpeng Li, Yunfeng Shao +3
Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from…