134 citations · 220 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…