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@VerbekeLab

VerbekeLab

Advancing the science and practice of data-driven decision-making
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Verbeke Lab

 

Research Topics

Topic Acronym
Causal Machine Learning CML
Machine Learning for Decision-Making MLDM
Cost-Sensitive Learning CSL
Credit Risk Management CRM
Fraud Risk Management FRM

 

Research

Year First Author Paper Code Venue Topic
2025 Rickermann C. Using representation balancing to learn conditional-average dose responses from clustered data Code Transactions on Machine Learning Research CML
Rickermann C. Can causal machine learning reveal individual bid responses of bank customers? — A study on mortgage loan applications in Belgium Code Decision Support Systems CML
2024 Van Belle J. Probabilistic forecasting with modified N-BEATS networks Code IEEE Transactions on Neural Learning Systems MLDM
De Vos S. Predicting Employee Turnover: Scoping and Benchmarking the State-of-the-Art Code Business Information Systems Engineering MLDM
Reusens M. A review and experimental evaluation of the state-of-the-art in text classification Code  Expert Systems with Applications MLDM
Deprez B. Network analytics for insurance fraud detection: a critical case study Code European Actuarial Journal FRM
De Vos S. Data-driven internal mobility: getting the job done with similarity regularization Code Knowledge Based Systems MLDM
Vanderschueren T. A new perspective on classification: Optimally allocating limited resources to uncertain tasks Code Decision Support Systems CSL
2023 Weytjens H. Timed Process Interventions: Causal Inference vs. Reinforcement Learning Code Lecture Notes in Business Information Processing CML
Vanderschueren T. Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time Code International Conference on Machine Learning CML
Scheltjens V. Client Recruitment for Federated Learning in ICU Length of Stay Prediction Code International Conference on e-Science MLDM
Vanderschueren T. NOFLITE: Learning to Predict Individual Treatment Effect Distributions Code Transactions on Machine Learning Research CML
De Bock K. Explainable Analytics in Operational Research: Methods, Applications and an Agenda for Future Research European Journal of Operational Research MLDM
Vanderschueren T. A new perspective on classification: Optimally allocating limited resources to uncertain tasks Code Decision Support Systems CSL
Vandervorst F. Claims fraud detection with uncertain labels Advances in Data Analysis and Classification FRM
Rickermann C. A decade of research on fraud analytics: challenges and methods Data Expert Systems with Applications FRM
De Vos S. Robust instance-dependent cost-sensitive classification Code Advances in Data Analysis and Classification CSL
Van Belle J. Improving forecast stability using deep learning Code International Journal of Forecasting MLDM
Vanderschueren T. Optimizing the preventive maintenance frequency with causal machine learning Code International Journal of Production Economics CML
Verbeke W. To do or not to do: Cost-sensitive causal decision-making Code European Journal of Operational Research CML
2022 Coenen L. Machine learning methods for short-term Probability of Default A comparison of classification, regression and ranking methods Journal of the Operational Research Society FRM
Verboven S. HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks Code Applied Intelligence MLDM
Berrevoets J. Individual treatment effect optimisation in dynamic environments Code Journal of Causal Inference CML
Devriendt F. Learning 2 Rank for uplift modeling IEEE Transactions on Knowledge and Data Engineering CML
Vandervorst F. Data misrepresentation detection for insurance underwriting fraud prevention Decision Support Systems FRM
Petrides G. Cost-sensitive learning for profit-driven credit scoring Data Journal of the Operational Research Society CSL
Vanderschueren T. Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies Code Information Systems CSL FRM
Petrides G. Cost-sensitive ensemble learning: a unifying framework Data Mining and Knowledge Discovery CSL
Hoppner S. Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud Using Lasso-Regularized Logistic Regression and Gradient Boosted Decision Trees Code European Journal of Operational Research CSL
Raymaekers J. Weight-of-evidence through shrinkage and spline binning for interpretable nonlinear classification Code Applied Soft Computing FRM CRM
2021 De Caigny A. Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach Industrial Marketing Management CML
Siozos-Rousoulis L. A study of the U.S. domestic air transportation network: Temporal evolution of network topology and robustness from 2001 to 2016 Code Journal of Transportation Safety and Security MLDM
Maldonado S. Profit-driven Churn Prediction for the Mutual Fund Industry: a Multi-segment Approach Omega CSL
Maldonado S. Redefining Profit Metrics for boosting Student Retention in Higher Education Decision Support Systems CSL
Devriendt F. Why you should stop predicting customer churn and start using uplift modeling Code Information Sciences CML CSL
Van Belle J. Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains European Journal of Operational Research MLDM
2020 Verboven S. Auto-encoders for strategic decision support Decision Support Systems MLDM
Olaya D. Uplift Modeling for Preventing Student Dropout in Higher Education Code Decision Support Systems CML
Benoit D. Article Commentary: On realizing the utopian potential of big data analytics for maximizing return on marketing investments Journal of Marketing Management CSL
Olaya D. A survey and benchmarking study of multitreatment uplift modeling Code Data Mining and Knowledge Discovery CML
Decauwer C. A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-world Conditions IEEE Transactions on Intelligent Transportation Systems MLDM

Popular repositories Loading

  1. template template Public template

    Python 4

  2. n-beats-s n-beats-s Public

    Pytorch implementation of N-BEATS-S from the paper "Improving forecast stability using deep learning"

    Jupyter Notebook 2 1

  3. N-N-BEATS N-N-BEATS Public

    Jupyter Notebook 1

  4. NetworkFraud_BiRank_M2V_SAGE NetworkFraud_BiRank_M2V_SAGE Public

    Forked from B-Deprez/NetworkFraud_BiRank_M2V_SAGE

    Python 1

  5. Causal-Pricing Causal-Pricing Public

    Python 1

  6. CostSensitiveLearning CostSensitiveLearning Public

    Forked from toonvds/CostSensitiveLearning

    Code for the paper "Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies".

    Python

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