57 citations · 217 across the 32 of their papers we have counts for
10 papers · 1 filter
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Baoxiang Wang +2
Communication is one of the core components for cooperative multi-agent reinforcement learning (MARL). The communication bandwidth, in many real applications, is always subject to…
Dialogue Inspectional Summarization with Factual Inconsistency Awareness
Leilei Gan, Yating Zhang, Kun Kuang +5
Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the out…
Unified Group Fairness on Federated Learning
Fengda Zhang, Kun Kuang, Yuxuan Liu +6
Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of e…
Stable Prediction on Graphs with Agnostic Distribution Shift
Shengyu Zhang, Kun Kuang, Jiezhong Qiu +5
Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with rand…
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the…
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong +5
Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representation…
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation co…
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Junkun Yuan, Anpeng Wu, Kun Kuang +4
Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confo…
Analysis and Applications of Class-wise Robustness in Adversarial Training
Qi Tian, Kun Kuang, Kelu Jiang +2
Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. However, previous works mainly focus on the overall robustnes…
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Jiahui Li, Kun Kuang, Baoxiang Wang +4
Centralized Training with Decentralized Execution (CTDE) has been a popular paradigm in cooperative Multi-Agent Reinforcement Learning (MARL) settings and is widely used in many re…