Author name cluster

Mahyar Salek

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3 papers

2 author rows

AAAI Conference 2013 Conference Paper

A Fast Bandit Algorithm for Recommendation to Users With Heterogenous Tastes

Pushmeet Kohli
Mahyar Salek
Greg Stoddard

We study recommendation in scenarios where there’s no prior information about the quality of content in the system. We present an online algorithm that continually optimizes recommendation relevance based on behavior of past users. Our method trades weaker theoretical guarantees in asymptotic performance than the state-ofthe-art for stronger theoretical guarantees in the online setting. We test our algorithm on real-world data collected from previous recommender systems and show that our algorithm learns faster than existing methods and performs equally well in the long-run.

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AAAI Conference 2013 Conference Paper

Hotspotting — A Probabilistic Graphical Model For Image Object Localization Through Crowdsourcing

Mahyar Salek
Yoram Bachrach
Peter Key

Object localization is an image annotation task which consists of finding the location of a target object in an image. It is common to crowdsource annotation tasks and aggregate responses to estimate the true annotation. While for other kinds of annotations consensus is simple and powerful, it cannot be applied to object localization as effectively due to the task’s rich answer space and inherent noise in responses. We propose a probabilistic graphical model to localize objects in images based on responses from the crowd. We improve upon natural aggregation methods such as the mean and the median by simultaneously estimating the difficulty level of each question and skill level of every participant. We empirically evaluate our model on crowdsourced data and show that our method outperforms simple aggregators both in estimating the true locations and in ranking participants by their ability. We also propose a simple adaptive sourcing scheme that works well for very sparse datasets.

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FOCS Conference 2010 Conference Paper

Frugal and Truthful Auctions for Vertex Covers, Flows and Cuts

David Kempe 0001
Mahyar Salek
Cristopher Moore

We study truthful mechanisms for hiring a team of agents in three classes of set systems: Vertex Cover auctions, How auctions, and cut auctions. For Vertex Cover auctions, the vertices are owned by selfish and rational agents, and the auctioneer wants to purchase a vertex cover from them. For k-flow auctions, the edges are owned by the agents, and the auctioneer wants to purchase k edge-disjoint s-t paths, for given s and t. In the same setting, for cut auctions, the auctioneer wants to purchase an s-t cut. Only the agents know their costs, and the auctioneer needs to select a feasible set and payments based on bids made by the agents. We present constant-competitive truthful mechanisms for all three set systems. That is, the maximum overpayment of the mechanism is within a constant factor of the maximum overpayment of any truthful mechanism, for every set system in the class. The mechanism for Vertex Cover is based on scaling each bid by a multiplier derived from the dominant eigenvector of a certain matrix. The mechanism for k-flows prunes the graph to be minimally (k + 1)-connected, and then applies the Vertex Cover mechanism. Similarly, the mechanism for cuts contracts the graph until all s-t paths have length exactly 2, and then applies the Vertex Cover mechanism.

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