Search Results for author: Shreyas Sekar

Found 6 papers, 0 papers with code

Learning Product Rankings Robust to Fake Users

no code implementations10 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.

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

no code implementations6 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.

Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback

no code implementations11 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.

Multi-Armed Bandits Q-Learning

Truthful Mechanisms for Matching and Clustering in an Ordinal World

no code implementations13 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.

Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching and Clustering

no code implementations17 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.

Computing Stable Coalitions: Approximation Algorithms for Reward Sharing

no code implementations27 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?".

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