601 citations · 2.1k across the 56 of their papers we have counts for
25 papers · 1 filter
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
Alexander Buchholz, Ben London, Giuseppe di Benedetto +1
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. Unfortunately, widely used off-p…
Boosted Off-Policy Learning
Ben London, Levi Lu, Ted Sandler +1
We propose the first boosting algorithm for off-policy learning from logged bandit feedback. Unlike existing boosting methods for supervised learning, our algorithm directly optimi…
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits
Aaron David Tucker, Thorsten Joachims
Methods for offline A/B testing and counterfactual learning are seeing rapid adoption in search and recommender systems, since they allow efficient reuse of existing log data. Howe…
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito, Thorsten Joachims
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. U…
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 in Ranking under Uncertainty
Ashudeep Singh, David Kempe, Thorsten Joachims
Fairness has emerged as an important consideration in algorithmic decision-making. Unfairness occurs when an agent with higher merit obtains a worse outcome than an agent with lowe…