activity
20172021
most citedLearning Efficient Multi-agent Communication: An Information Bottleneck Approach

38 citations · 76 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2021★ 4 cited

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Wenyu Liu, Gaofeng Ren, Runsheng Yu +3

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality ima…

cs.LG2021★ 16 cited

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

Wei Qiu, Xinrun Wang, Runsheng Yu +5

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (…

cs.LG2021★ 2 cited

DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu +3

In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles. GA…

cs.IR2020★ 1 cited

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

Runsheng Yu, Yu Gong, Xu He +4

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…

cs.LG2020★ 9 cited

Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication

Xu He, Bo An, Yanghua Li +6

With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items wi…

cs.AI2019★ 1 cited

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

Runsheng Yu, Zhenyu Shi, Xinrun Wang +5

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized t…