128 citations · 523 across the 53 of their papers we have counts for
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Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Stochastic learning to rank (LTR) is a recent branch in the LTR field that concerns the optimization of probabilistic ranking models. Their probabilistic behavior enables certain r…
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression
Sami Jullien, Romain Deffayet, Jean-Michel Renders +2
Distributional reinforcement learning (RL) has proven useful in multiple benchmarks as it enables approximating the full distribution of returns and extracts rich feedback from env…
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…