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cs.LG2021★ 41 cited
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression
Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
Gradient Boosting Machines (GBM) are hugely popular for solving tabular data problems. However, practitioners are not only interested in point predictions, but also in probabilisti…
cs.LG2021★ 7 cited
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
Harrie Oosterhuis, Maarten de Rijke
Existing work in counterfactual Learning to Rank (LTR) has focussed on optimizing feature-based models that predict the optimal ranking based on document features. LTR methods base…
cs.LG2020★ 13 cited
Accelerated Convergence for Counterfactual Learning to Rank
Rolf Jagerman, Maarten de Rijke
Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system. Employing such an offline learning…