381 citations · 2.3k across the 89 of their papers we have counts for
23 papers · 1 filter
Multidimensional Dynamic Pricing for Welfare Maximization
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman +1
We study the problem of a seller dynamically pricing distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an u…
Adaptive Learning with Robust Generalization Guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim +2
The traditional notion of generalization---i.e., learning a hypothesis whose empirical error is close to its true error---is surprisingly brittle. As has recently been noted in [DF…
Coordination Complexity: Small Information Coordinating Large Populations
Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan +2
We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among $n…
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Shahin Jabbari, Ryan Rogers, Aaron Roth +1
We define and study the problem of predicting the solution to a linear program (LP) given only partial information about its objective and constraints. This generalizes the problem…
Privacy for the Protected (Only)
Michael Kearns, Aaron Roth, Zhiwei Steven Wu +1
Motivated by tensions between data privacy for individual citizens, and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a compu…
Watch and Learn: Optimizing from Revealed Preferences Feedback
Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu
A Stackelberg game is played between a leader and a follower. The leader first chooses an action, then the follower plays his best response. The goal of the leader is to pick the a…