Despite the growing popularity of machine-learning techniques in
decisio...
Estimating the effects of treatments with an associated dose on an insta...
In lending, where prices are specific to both customers and products, ha...
The shift from the understanding and prediction of processes to their
op...
Machine learning (ML) holds great potential for accurately forecasting
t...
Machine and deep learning methods for medical and healthcare application...
The literature on fraud analytics and fraud detection has seen a substan...
Machine maintenance is a challenging operational problem, where the goal...
A central problem in business concerns the optimal allocation of limited...
In many practical applications, such as fraud detection, credit risk mod...
Causal classification models are adopted across a variety of operational...
Multi-task learning (MTL) can improve performance on a task by sharing
r...
Classification is a well-studied machine learning task which concerns th...
Over the years, a plethora of cost-sensitive methods have been proposed ...
Card transaction fraud is a growing problem affecting card holders world...
In the majority of executive domains, a notion of normality is involved ...
Uplift modeling has effectively been used in fields such as marketing an...
Social network analytics methods are being used in the telecommunication...
Relational learning in networked data has been shown to be effective in ...
Applying causal inference models in areas such as economics, healthcare ...
Uplift modeling requires experimental data, preferably collected in rand...