Search Results for author: Sitao Luan

Found 9 papers, 3 papers with code

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

no code implementations20 Aug 2020 Sitao Luan, 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 filters the neighborhood node information.

Graph Classification Node Classification

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

no code implementations20 Aug 2020 Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning paradigms, are pervasively thought to be caused by the limitations of the GCN layers, including insufficient expressive power, etc.

Revisit Policy Optimization in Matrix Form

no code implementations19 Sep 2019 Sitao Luan, Xiao-Wen Chang, Doina Precup

In tabular case, when the reward and environment dynamics are known, policy evaluation can be written as $\bm{V}_{\bm{\pi}} = (I - \gamma P_{\bm{\pi}})^{-1} \bm{r}_{\bm{\pi}}$, where $P_{\bm{\pi}}$ is the state transition matrix given policy ${\bm{\pi}}$ and $\bm{r}_{\bm{\pi}}$ is the reward signal given ${\bm{\pi}}$.

Model-based Reinforcement Learning reinforcement-learning

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

1 code implementation NeurIPS 2019 Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.

Node Classification

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

2 code implementations25 Apr 2019 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 learn the value of a given policy, as well as algorithms which learn how to improve policies.


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