Summary of Novelty-guided Data Reuse For Efficient and Diversified Multi-agent Reinforcement Learning, by Yangkun Chen et al.
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learningby Yangkun Chen, Kai Yang, Jian…
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learningby Yangkun Chen, Kai Yang, Jian…
PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Timeby Alireza Pourali, Arian Boukani,…
Generalized Back-Stepping Experience Replay in Sparse-Reward Environmentsby Guwen Lyu, Masahiro SatoFirst submitted to arxiv on:…
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branchby Shengyu Feng, Yiming YangFirst submitted to arxiv…
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoningby Zheyuan Zhang, Yiyang…
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learningby Shuaijun Chen, Omid Tavallaie, Niousha Nazemi,…
FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHFby Flint Xiaofeng Fan, Cheston Tan,…
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equationby Yanna Ding, Zijie Huang, Xiao Shou,…
Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Modelsby Nahian Ahmed, Mark Roth,…
In-context Continual Learning Assisted by an External Continual Learnerby Saleh Momeni, Sahisnu Mazumder, Zixuan Ke,…