Jon Feldman

Online Stochastic Matching: Beating 1-1/e

We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp, Vazirani and Vazirani gives an approximation ratio of

Online stochastic packing applied to display ad allocation

1-{1\over e} \simeq 0.632

Online Ad Assignment with Free Disposal

, a very familiar bound that holds for many online problems; further, the bound is tight in this case. In the online, stochastic case when nodes are drawn repeatedly from a known distribution, the greedy algorithm matches this approximation ratio, but still, no algorithm is known that beats the

LP decoding corrects a constant fraction of errors

1 - {1\over e}

A New Linear Programming Approach to Decoding Linear Block Codes

bound.Our main result is a

Bidding to the top: VCG and equilibria of position-based auctions

0.67

Learning mixtures of product distributions over discrete domains

-approximation online algorithm for stochastic bipartite matching, breaking this

Nonlinear programming approaches to decoding low-density parity-check codes

1 - {1\over e}

A Truthful Mechanism for Offline Ad Slot Scheduling

barrier. Furthermore, we show that no online algorithm can produce a

Computing an optimal orientation of a balanced decomposition tree for linear arrangement problems

1-\epsilon