Search Results for author: Tengfei Liu

Found 10 papers, 3 papers with code

Automated Metaheuristic Algorithm Design with Autoregressive Learning

no code implementations6 May 2024 Qi Zhao, Tengfei Liu, Bai Yan, Qiqi Duan, Jian Yang, Yuhui Shi

To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms.

A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units

1 code implementation10 Mar 2024 Liyue Chen, Jiangyi Fang, Tengfei Liu, Shaosheng Cao, Leye Wang

Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing.

On provable privacy vulnerabilities of graph representations

no code implementations6 Feb 2024 Ruofan Wu, Guanhua Fang, Qiying Pan, Mingyang Zhang, Tengfei Liu, Weiqiang Wang, Wenbiao Zhao

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations.

Graph Representation Learning

Privacy-preserving design of graph neural networks with applications to vertical federated learning

no code implementations31 Oct 2023 Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).

Graph Representation Learning Management +2

Self-supervision meets kernel graph neural models: From architecture to augmentations

no code implementations17 Oct 2023 Jiawang Dan, Ruofan Wu, Yunpeng Liu, Baokun Wang, Changhua Meng, Tengfei Liu, Tianyi Zhang, Ningtao Wang, Xing Fu, Qi Li, Weiqiang Wang

Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).

Data Augmentation Graph Classification +2

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks

no code implementations18 Sep 2023 Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, Weiqiang Wang

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

1 code implementation4 Aug 2023 Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.

Missing Elements Temporal Knowledge Graph Completion

Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty

no code implementations30 Nov 2022 Si Wu, Tengfei Liu, Magnus Egerstedt, Zhong-Ping Jiang

Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues.

Collision Avoidance Continuous Control

Latent Tree Models for Hierarchical Topic Detection

1 code implementation21 May 2016 Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K. M. Poon, Zhourong Chen, Farhan Khawar

The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below.

Clustering Topic Models

A Survey on Latent Tree Models and Applications

no code implementations4 Feb 2014 Raphaël Mourad, Christine Sinoquet, Nevin L. Zhang, Tengfei Liu, Philippe Leray

In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences.


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