134 citations · 220 across the 14 of their papers we have counts for
15 papers
Multi-task Optimization Based Co-training for Electricity Consumption Prediction
Hui Song, A. K. Qin, Chenggang Yan
Real-world electricity consumption prediction may involve different tasks, e.g., prediction for different time steps ahead or different geo-locations. These tasks are often solved…
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems
Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar +1
Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However…
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Maoguo Gong, Yuan Gao, Yue Wu +1
Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…
Learning Enhanced Optimisation for Routing Problems
Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar +2
Deep learning approaches have shown promising results in solving routing problems. However, there is still a substantial gap in solution quality between machine learning and operat…
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
Hui Song, A. K. Qin, Flora D. Salim
Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected…
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Yuan Gao, Jiawei Li, Maoguo Gong +2
Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approache…
An attention-based unsupervised adversarial model for movie review spam detection
Yuan Gao, Maoguo Gong, Yu Xie +1
With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and decep…
Learning Vehicle Routing Problems using Policy Optimisation
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
Deep reinforcement learning (DRL) has been used to learn effective heuristics for solving complex combinatorial optimisation problem via policy networks and have demonstrated promi…
Learning to Optimise General TSP Instances
Nasrin Sultana, Jeffrey Chan, A. K. Qin +1
The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving effor…
Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
Wenfeng Liu, Maoguo Gong, Zedong Tang +1
Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed conv…
Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments
Prabath Abeysekara, Hai Dong, A. K. Qin
Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based I…
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization
Boyu Zhang, A. K. Qin, Hong Pan +1
Conventional DNN training paradigms typically rely on one training set and one validation set, obtained by partitioning an annotated dataset used for training, namely gross trainin…
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors
Ziqiang Li, Hong Pan, Yaping Zhu +1
Precise segmentation of organs and tumors plays a crucial role in clinical applications. It is a challenging task due to the irregular shapes and various sizes of organs and tumors…
Location-Centered House Price Prediction: A Multi-Task Learning Approach
Guangliang Gao, Zhifeng Bao, Jie Cao +3
Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered predictio…
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Yuan Yuan, Yew-Soon Ong, Liang Feng +7
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be s…