Search Results for author: Shunyu Liu

Found 21 papers, 14 papers with code

Simple Graph Condensation

no code implementations22 Mar 2024 Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, Tongya Zheng

The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph.

Advantage-Aware Policy Optimization for Offline Reinforcement Learning

no code implementations12 Mar 2024 Yunpeng Qing, Shunyu Liu, Jingyuan Cong, KaiXuan Chen, Yihe Zhou, Mingli Song

Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the Out-Of-Distribution (OOD) problem.

D4RL reinforcement-learning +1

COLA: Cross-city Mobility Transformer for Human Trajectory Simulation

1 code implementation4 Mar 2024 Yu Wang, Tongya Zheng, Yuxuan Liang, Shunyu Liu, Mingli Song

To address these challenges, we have tailored a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework by effectively transferring cross-city knowledge for human trajectory simulation.

CoLA Transfer Learning

Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models

1 code implementation23 Feb 2024 Shunyu Liu, Jie zhou, Qunxi Zhu, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Disentangled Condensation for Large-scale Graphs

1 code implementation18 Jan 2024 Zhenbang Xiao, Shunyu Liu, Yu Wang, Tongya Zheng, Mingli Song

Graph condensation has emerged as an intriguing technique to provide Graph Neural Networks for large-scale graphs with a more compact yet informative small graph to save the expensive costs of large-scale graph learning.

Graph Learning Link Prediction +1

Powerformer: A Section-adaptive Transformer for Power Flow Adjustment

no code implementations5 Jan 2024 KaiXuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Jie Song, Mingli Song

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.

Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning

no code implementations28 Nov 2023 Yaoquan Wei, Shunyu Liu, Jie Song, Tongya Zheng, KaiXuan Chen, Yong Wang, Mingli Song

Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise).

Atari Games

Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket

1 code implementation5 Aug 2023 Yuwen Wang, Shunyu Liu, KaiXuan Chen, Tongtian Zhu, Ji Qiao, Mengjie Shi, Yuanyu Wan, Mingli Song

Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large input graphs while preserving original performance.

Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

no code implementations15 Jun 2023 Yu Wang, Tongya Zheng, Shunyu Liu, KaiXuan Chen, Zunlei Feng, Yunzhi Hao, Mingli Song

The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.

Curricular Subgoals for Inverse Reinforcement Learning

1 code implementation14 Jun 2023 Shunyu Liu, Yunpeng Qing, Shuqi Xu, Hongyan Wu, Jiangtao Zhang, Jingyuan Cong, Tianhao Chen, YunFu Liu, Mingli Song

Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning.

Autonomous Driving D4RL +2

Message-passing selection: Towards interpretable GNNs for graph classification

no code implementations3 Jun 2023 Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song

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' baselines.

Graph Classification

Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization

1 code implementation31 May 2023 KaiXuan Chen, Shunyu Liu, Tongtian Zhu, Tongya Zheng, Haofei Zhang, Zunlei Feng, Jingwen Ye, Mingli Song

Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.

Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

1 code implementation27 May 2023 Yihe Zhou, Shunyu Liu, Yunpeng Qing, KaiXuan Chen, Tongya Zheng, Yanhao Huang, Jie Song, Mingli Song

Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training.

Multi-agent Reinforcement Learning reinforcement-learning +2

Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

1 code implementation23 Nov 2022 Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng, KaiXuan Chen, Tongtian Zhu, Zunlei Feng, Mingli Song

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.

Contrastive Learning SMAC+

A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

1 code implementation12 Nov 2022 Yunpeng Qing, Shunyu Liu, Jie Song, Huiqiong Wang, Mingli Song

In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods.

reinforcement-learning Reinforcement Learning (RL)

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

1 code implementation8 Jul 2022 Shunyu Liu, Jie Song, Yihe Zhou, Na Yu, KaiXuan Chen, Zunlei Feng, Mingli Song

In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.

Multi-agent Reinforcement Learning reinforcement-learning +1

Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework

1 code implementation5 Jul 2022 Shunyu Liu, KaiXuan Chen, Na Yu, Jie Song, Zunlei Feng, Mingli Song

Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

1 code implementation12 May 2022 KaiXuan Chen, Shunyu Liu, Na Yu, Rong Yan, Quan Zhang, Jie Song, Zunlei Feng, Mingli Song

As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.

Binary Classification Graph Representation Learning +1

Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN

no code implementations16 Dec 2021 Gengshi Han, Shunyu Liu, KaiXuan Chen, Na Yu, Zunlei Feng, Mingli Song

This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.

Generative Adversarial Network

Distribution Knowledge Embedding for Graph Pooling

1 code implementation29 Sep 2021 KaiXuan Chen, Jie Song, Shunyu Liu, Na Yu, Zunlei Feng, Gengshi Han, Mingli Song

A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.

Representation Learning

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