134 citations · 210 across the 12 of their papers we have counts for
14 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…