6 papers · 1 filter
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3
Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environments and preference settings.…
Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Sampling
Bahrul Ilmi Nasution
Synthetic tabular data enables microdata sharing in regulated domains, yet deploying continuous-time generative models requires balancing analytical utility, disclosure risk, and c…
Flow Matching for Tabular Data Synthesis
Bahrul Ilmi Nasution, Floor Eijkelboom, Mark Elliot +2
Synthetic data generation is an important tool for privacy-preserving data sharing. Although diffusion models have set recent benchmarks, flow matching (FM) offers a promising alte…
MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
Rifny Rachman, Josh Tingey, Richard Allmendinger +3
Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and comp…
Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Bahrul Ilmi Nasution, Mark Elliot, Richard Allmendinger
Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but strugg…
Multi-objective evolutionary GAN for tabular data synthesis
Nian Ran, Bahrul Ilmi Nasution, Claire Little +2
Synthetic data has a key role to play in data sharing by statistical agencies and other generators of statistical data products. Generative Adversarial Networks (GANs), typically a…