11 citations · 20 across the 6 of their papers we have counts for
7 papers
Is Learning in Games Good for the Learners?
William Brown, Jon Schneider, Kiran Vodrahalli
We consider a number of questions related to tradeoffs between reward and regret in repeated gameplay between two agents. To facilitate this, we introduce a notion of $\textit{gene…
Online Recommendations for Agents with Discounted Adaptive Preferences
Arpit Agarwal, William Brown
We consider a bandit recommendations problem in which an agent's preferences (representing selection probabilities over recommended items) evolve as a function of past selections,…
Learning in Multi-Player Stochastic Games
William Brown
We consider the problem of simultaneous learning in stochastic games with many players in the finite-horizon setting. While the typical target solution for a stochastic game is a N…
Diversified Recommendations for Agents with Adaptive Preferences
Arpit Agarwal, William Brown
When an Agent visits a platform recommending a menu of content to select from, their choice of item depends not only on fixed preferences, but also on their prior engagements with…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns +4
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like -way marginals, subject to differential privacy. Our…