396 citations · 489 across the 14 of their papers we have counts for
14 papers
Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
Joseph Suárez, Kyoung Whan Choe, David Bloomin +22
We present the results of the NeurIPS 2023 Neural MMO Competition, which attracted over 200 participants and submissions. Participants trained goal-conditional policies that genera…
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Joseph Suárez, Phillip Isola, Kyoung Whan Choe +15
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define…
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Kai Yang, Jian Tao, Jiafei Lyu +6
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns…
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Han Liu, Xingshuo Huang, Xiaotong Zhang +6
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model predictio…
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning
Guangyuan Shi, Qimai Li, Wenlong Zhang +2
A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gra…
Simple yet Effective Gradient-Free Graph Convolutional Networks
Yulin Zhu, Xing Ai, Qimai Li +2
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models,…
Multi-Agent Path Finding via Tree LSTM
Yuhao Jiang, Kunjie Zhang, Qimai Li +2
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flat…
Modeling User Behavior with Graph Convolution for Personalized Product Search
Fan Lu, Qimai Li, Bo Liu +7
User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance b…
Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
Han Liu, Xianchao Zhang, Xiaotong Zhang +2
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, the…
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Qimai Li, Xiaotong Zhang, Han Liu +2
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that com…
Attributed Graph Clustering via Adaptive Graph Convolution
Xiaotong Zhang, Han Liu, Qimai Li +1
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that g…
Label Efficient Semi-Supervised Learning via Graph Filtering
Qimai Li, Xiao-Ming Wu, Han Liu +2
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and u…
Large Margin Few-Shot Learning
Yong Wang, Xiao-Ming Wu, Qimai Li +4
The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-sh…
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, Xiao-Ming Wu
Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph con…