1 code implementation • 24 Jan 2022 • Rohan Tangri, Danilo P. Mandic, Anthony G. Constantinides
Reinforcement learning is increasingly finding success across domains where the problem can be represented as a Markov decision process.
no code implementations • 11 May 2021 • Bruno Scalzo Dees, Yao Lei Xu, Anthony G. Constantinides, Danilo P. Mandic
Finally, we also explore the application of modern deep learning models, such as graph neural networks and hyper-graph neural networks, as general purpose models for the modelling and forecasting of underground data, especially in the context of the morning and evening rush hours.
no code implementations • 2 Jan 2020 • Ljubisa Stankovic, Danilo Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution.
no code implementations • 12 Oct 2019 • Bruno Scalzo Dees, Ljubisa Stankovic, Anthony G. Constantinides, Danilo P. Mandic
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure.