5 citations · 7 across the 6 of their papers we have counts for
6 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…
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems
Fengyu Li, Sarah Dean
Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of select…
Online Convex Optimization with Unbounded Memory
Raunak Kumar, Sarah Dean, Robert Kleinberg
Online convex optimization (OCO) is a widely used framework in online learning. In each round, the learner chooses a decision in a convex set and an adversary chooses a convex loss…
Modeling Content Creator Incentives on Algorithm-Curated Platforms
Jiri Hron, Karl Krauth, Michael I. Jordan +2
Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt…
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff +2
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in t…
Preference Dynamics Under Personalized Recommendations
Sarah Dean, Jamie Morgenstern
Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not chan…