62 citations · 86 across the 4 of their papers we have counts for
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RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Safe Exploration for Optimizing Contextual Bandits
Rolf Jagerman, Ilya Markov, Maarten de Rijke
Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, etc. However, existing learning me…
Unbiased Learning to Rank: Counterfactual and Online Approaches
Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
This tutorial covers and contrasts the two main methodologies in unbiased Learning to Rank (LTR): Counterfactual LTR and Online LTR. There has long been an interest in LTR from use…
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
Learning to Rank (LTR) from user interactions is challenging as user feedback often contains high levels of bias and noise. At the moment, two methodologies for dealing with bias p…
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
Rolf Jagerman, Julia Kiseleva, Maarten de Rijke
List-wise learning to rank methods are considered to be the state-of-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learn…