14 citations · 59 across the 28 of their papers we have counts for
9 papers · 1 filter
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Yaoquan Wei, Shunyu Liu, Jie Song +4
Action advising endeavors to leverage supplementary guidance from expert teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement Learning (DRL). Previous age…
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue +2
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption bec…
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Kaixuan Chen, Shunyu Liu, Tongtian Zhu +5
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data. However, existing GNNs typically ignore crucial struct…
Message-passing selection: Towards interpretable GNNs for graph classification
Wenda Li, Kaixuan Chen, Shunyu Liu +5
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' b…
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
Yu Wang, Tongya Zheng, Shunyu Liu +4
Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human…
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue +2
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. Thi…
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
Tongtian Zhu, Fengxiang He, Kaixuan Chen +2
Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on massive devices simultaneously without the control of a central server. However, existing theorie…
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Yihe Zhou, Shunyu Liu, Yunpeng Qing +4
Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use…
Schema Inference for Interpretable Image Classification
Haofei Zhang, Mengqi Xue, Xiaokang Liu +3
In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural net…