57 citations · 74 across the 6 of their papers we have counts for
6 papers
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
Lequn Wang, Akshay Krishnamurthy, Aleksandrs Slivkins
We consider offline policy optimization (OPO) in contextual bandits, where one is given a fixed dataset of logged interactions. While pessimistic regularizers are typically used to…
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
Lequn Wang, Thorsten Joachims
Many large-scale recommender systems consist of two stages. The first stage efficiently screens the complete pool of items for a small subset of promising candidates, from which th…
Improving Screening Processes via Calibrated Subset Selection
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
Many selection processes such as finding patients qualifying for a medical trial or retrieval pipelines in search engines consist of multiple stages, where an initial screening sta…
Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun +1
Contextual bandit algorithms have become widely used for recommendation in online systems (e.g. marketplaces, music streaming, news), where they now wield substantial influence on…
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
Lequn Wang, Thorsten Joachims
Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a…
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
Yi Su, Lequn Wang, Michele Santacatterina +1
The ability to perform offline A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommend…