Portrait of Sitao Luan is unavailable

Sitao Luan

Postdoctorate - McGill University
Supervisor
Guillaume Rabusseau
Co-supervisor
Reihaneh Rabbany
Research Topics
AI for Science
Data Mining
Graph Neural Networks
Reasoning
Google Scholar
GitHub

Publications

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
Sitao Luan
Harry Zhao
Mingde Zhao
Chenqing Hua
Xiao-Wen Chang
Doina Precup
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filte… (see more)rs the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.
2022-11-22
NeurIPS.cc/2022/Workshop/GLFrontiers (published)
doi.org
openreview.net
When Do We Need GNN for Node Classification?
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Doina Precup
2022-10-30
arXiv.org (preprint)
doi.org
When Do We Need GNN for Node Classification?
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Doina Precup
2022-10-30
arXiv.org (preprint)
doi.org
Revisiting Heterophily For Graph Neural Networks
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Harry Zhao
Mingde Zhao
Shuyuan Zhang
Xiao-Wen Chang
Doina Precup
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily … (see more)assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared to NNs is not satisfactory. Heterophily has been considered the main cause of this empirical observation and numerous works have been put forward to address it. In this paper, we first revisit the widely used homophily metrics and point out that their consideration of only graph-label consistency is a shortcoming. Then, we study heterophily from the perspective of post-aggregation node similarity and define new homophily metrics, which are potentially advantageous compared to existing ones. Based on this investigation, we prove that some harmful cases of heterophily can be effectively addressed by local diversification operation. Then, we propose the Adaptive Channel Mixing (ACM), a framework to adaptively exploit aggregation, diversification and identity channels node-wisely to extract richer localized information for diverse node heterophily situations. ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers. When evaluated on 10 benchmark node classification tasks, ACM-augmented baselines consistently achieve significant performance gain, exceeding state-of-the-art GNNs on most tasks without incurring significant computational burden.
openreview.net
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Harry Zhao
Mingde Zhao
Zhen Liu
Sitao Luan
Shuyuan Zhang
Doina Precup
Yoshua Bengio
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during plan… (see more)ning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.
openreview.net
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
Mingde Zhao
Sitao Luan
Ian Porada
Xiao-Wen Chang
Doina Precup
Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that lea… (see more)rn the value of a given policy, as well as algorithms which learn how to improve policies. TD-learning with eligibility traces provides a way to boost sample efficiency by temporal credit assignment, i.e. deciding which portion of a reward should be assigned to predecessor states that occurred at different previous times, controlled by a parameter
2020-01-01
AAMAS (published)
dblp.uni-trier.de