601 citations · 2.1k across the 56 of their papers we have counts for
57 papers
Ranking with Long-Term Constraints
Kianté Brantley, Zhichong Fang, Sarah Dean +1
The feedback that users provide through their choices (e.g., clicks, purchases) is one of the most common types of data readily available for training search and recommendation alg…
Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters
Jinsook Lee, Bradon Thymes, Joyce Zhou +2
University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender)…
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
Yuta Saito, Qingyang Ren, Thorsten Joachims
We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action spaces where conventional importance-weighting approaches suffer from excessive varianc…
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions
Hansol Lee, René F. Kizilcec, Thorsten Joachims
A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized t…
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…