9 citations · 30 across the 6 of their papers we have counts for
6 papers
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…
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Xu He, Bo An, Yanghua Li +4
Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limite…
Learning Behaviors with Uncertain Human Feedback
Xu He, Haipeng Chen, Bo An
Human feedback is widely used to train agents in many domains. However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the op…
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…
Collaboration based Multi-Label Learning
Lei Feng, Bo An, Shuo He
It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, whic…
Partial Label Learning with Self-Guided Retraining
Lei Feng, Bo An
Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attemp…