18 citations · 96 across the 23 of their papers we have counts for
23 papers
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Taichi Liu, Chen Gao, Zhenyu Wang +4
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem…
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Bowen Wang, Chen Liang, Jiaze Wang +7
Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with…
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Hao Chen +5
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network…
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
Junjie Wang, Yao Mu, Dong Li +6
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse…
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Haozhi Wang, Qing Wang, Yunfeng Shao +3
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…