no code implementations • 11 Mar 2018 • Tanner Fiez, Shreyas Sekar, Lillian J. Ratliff
We analyze these algorithms under two types of smoothed reward feedback at the end of each epoch: a reward that is the discount-average of the discounted rewards within an epoch, and a reward that is the time-average of the rewards within an epoch.
no code implementations • 13 Oct 2016 • Elliot Anshelevich, Shreyas Sekar
We study truthful mechanisms for matching and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information.
no code implementations • 17 Dec 2015 • Elliot Anshelevich, Shreyas Sekar
Using our algorithms for matching as a black-box, we also design new approximation algorithms for other closely related problems: these include a a 3. 2-approximation for the problem of clustering agents into equal sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a 2. 14-approximation algorithm for Max TSP.
no code implementations • 27 Aug 2015 • Elliot Anshelevich, Shreyas Sekar
Consider a setting where selfish agents are to be assigned to coalitions or projects from a fixed set P. Each project k is characterized by a valuation function; v_k(S) is the value generated by a set S of agents working on project k. We study the following classic problem in this setting: "how should the agents divide the value that they collectively create?".
no code implementations • 6 Jul 2018 • Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge.
no code implementations • 10 Sep 2020 • Negin Golrezaei, Vahideh Manshadi, Jon Schneider, Shreyas Sekar
We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users.