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09/21/2023
Uplift vs. predictive modeling: a theoretical analysis
Despite the growing popularity of machine-learning techniques in decisio...
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09/07/2023
Learning continuous-valued treatment effects through representation balancing
Estimating the effects of treatments with an associated dose on an insta...
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09/07/2023
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
In lending, where prices are specific to both customers and products, ha...
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06/07/2023
Timing Process Interventions with Causal Inference and Reinforcement Learning
The shift from the understanding and prediction of processes to their op...
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06/07/2023
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Machine learning (ML) holds great potential for accurately forecasting t...
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04/28/2023
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
Machine and deep learning methods for medical and healthcare application...
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12/07/2022
Fraud Analytics: A Decade of Research – Organizing Challenges and Solutions in the Field
The literature on fraud analytics and fraud detection has seen a substan...
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06/03/2022
Prescriptive maintenance with causal machine learning
Machine maintenance is a challenging operational problem, where the goal...
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02/09/2022
A new perspective on classification: optimally allocating limited resources to uncertain tasks
A central problem in business concerns the optimal allocation of limited...
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01/05/2021
Weight-of-evidence 2.0 with shrinkage and spline-binning
In many practical applications, such as fraud detection, credit risk mod...
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01/05/2021
To do or not to do: cost-sensitive causal decision-making
Causal classification models are adopted across a variety of operational...
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08/26/2020
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks
Multi-task learning (MTL) can improve performance on a task by sharing r...
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07/24/2020
The foundations of cost-sensitive causal classification
Classification is a well-studied machine learning task which concerns th...
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07/14/2020
Misclassification cost-sensitive ensemble learning: A unifying framework
Over the years, a plethora of cost-sensitive methods have been proposed ...
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05/05/2020
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Card transaction fraud is a growing problem affecting card holders world...
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05/03/2020
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved ...
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02/14/2020
Learning to rank for uplift modeling
Uplift modeling has effectively been used in fields such as marketing an...
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01/18/2020
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
Social network analytics methods are being used in the telecommunication...
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01/18/2020
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
Relational learning in networked data has been shown to be effective in ...
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10/16/2019
Optimising Individual-Treatment-Effect Using Bandits
Applying causal inference models in areas such as economics, healthcare ...
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02/01/2019