337 citations · 399 across the 25 of their papers we have counts for
26 papers
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Jun Chen, Yongchao Liu, Pengyu Qiu +6
Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop questions, multi-turn retrieval-au…
scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing
Ping Xu, Pengjiang Li, Tian Du +6
Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. However, existing methods focus on mi…
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning
Hao Dong, Ziyue Qiao, Zhiyuan Ning +4
Temporal Knowledge Graphs (TKGs), as an extension of static Knowledge Graphs (KGs), incorporate the temporal feature to express the transience of knowledge by describing when facts…
Rethinking Graph Contrastive Learning through Relative Similarity Preservation
Zhiyuan Ning, Pengfei Wang, Ziyue Qiao +2
Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this…
Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +7
Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream ma…
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
Weiliang Zhang, Xiaohan Huang, Yi Du +5
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrappe…
FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
Tianqi He, Xiaohan Huang, Yi Du +6
Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspec…
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy
Xiaohan Huang, Dongjie Wang, Zhiyuan Ning +6
Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream…
Towards Continuous Reuse of Graph Models via Holistic Memory Diversification
Ziyue Qiao, Junren Xiao, Qingqiang Sun +3
This paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continuously train a graph model to handle new tasks wh…
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Wei Ju, Yiyang Gu, Zhengyang Mao +5
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph co…
Semi-supervised Domain Adaptation in Graph Transfer Learning
Ziyue Qiao, Xiao Luo, Meng Xiao +3
As a specific case of graph transfer learning, unsupervised domain adaptation on graphs aims for knowledge transfer from label-rich source graphs to unlabeled target graphs. Howeve…
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
Meng Xiao, Min Wu, Ziyue Qiao +4
The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency. The agency wil…
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Hao Dong, Zhiyuan Ning, Pengyang Wang +4
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of work…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been…
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Meng Xiao, Dongjie Wang, Min Wu +5
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data…
Kernel-based Substructure Exploration for Next POI Recommendation
Wei Ju, Yifang Qin, Ziyue Qiao +4
Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important rol…
Graph Soft-Contrastive Learning via Neighborhood Ranking
Zhiyuan Ning, Pengfei Wang, Pengyang Wang +5
Graph Contrastive Learning (GCL) has emerged as a promising approach in the realm of graph self-supervised learning. Prevailing GCL methods mainly derive from the principles of con…
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
Meng Xiao, Min Wu, Ziyue Qiao +4
Funding agencies are largely relied on a topic matching between domain experts and research proposals to assign proposal reviewers. As proposals are increasingly interdisciplinary,…
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Meng Xiao, Ziyue Qiao, Yanjie Fu +5
The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has b…
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
Meng Xiao, Ziyue Qiao, Yanjie Fu +5
The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has bee…
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
Ziyue Qiao, Yanjie Fu, Pengyang Wang +5
With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become in…
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
Meng Xiao, Ziyue Qiao, Yanjie Fu +2
To advance the development of science and technology, research proposals are submitted to open-court competitive programs developed by government agencies (e.g., NSF). Proposal cla…
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
Zhengzheng Tang, Ziyue Qiao, Xuehai Hong +4
Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizab…
LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
Zhiyuan Ning, Ziyue Qiao, Hao Dong +2
Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models…