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Sergei Vassilvitskii

  1. Scalable K-Means++.

    Пт, 03/30/2012 - 15:01 — SomNambul
    Authors: Sergei Vassilvitskii, Andrea Vattani, Benjamin Moseley, Ravi Kumar, Bahman Bahmani
    Subjects: Databases
    Abstract

    Over half a century old and showing no signs of aging, k-means remains one of
    the most popular data processing algorithms. As is well-known, a proper
    initialization of k-means is crucial for obtaining a good final solution. The
    recently proposed k-means++ initialization algorithm achieves this, obtaining
    an initial set of centers that is provably close to the optimum solution. A
    major downside of the k-means++ is its inherent sequential nature, which limits
    its applicability to massive data: one must make k passes over the data to find
    a good initial set of centers.

  2. SHALE: An Efficient Algorithm for Allocation of Guaranteed Display Advertising.

    Пнд, 03/19/2012 - 15:02 — SomNambul
    Authors: Sergei Vassilvitskii, Peiji Chen, Wenjing Ma, Chandrashekhar Nagarajan, Erik Vee, Vijay Bharadwaj, John Tomlin, Jian Yang
    Subjects: Data Structures and Algorithms
    Abstract

    Motivated by the problem of optimizing allocation in guaranteed display
    advertising, we develop an efficient, lightweight method of generating a
    compact {\em allocation plan} that can be used to guide ad server decisions.
    The plan itself uses just O(1) state per guaranteed contract, is robust to
    noise, and allows us to serve (provably) nearly optimally. The optimization
    method we develop is scalable, with a small in-memory footprint, and working in
    linear time per iteration.

  3. Ad Serving Using a Compact Allocation Plan.

    Пнд, 03/19/2012 - 15:01 — SomNambul
    Authors: Sergei Vassilvitskii, Peiji Chen, Wenjing Ma, Srinath Mandalapu, Chandrashekhar Nagarajan, Jayavel Shanmugasundaram, Erik Vee, Manfai Yu, Jason Zien
    Subjects: Data Structures and Algorithms
    Abstract

    A large fraction of online display advertising is sold via guaranteed
    contracts: a publisher guarantees to the advertiser a certain number of user
    visits satisfying the targeting predicates of the contract. The publisher is
    then tasked with solving the ad serving problem - given a user visit, which of
    the thousands of matching contracts should be displayed, so that by the
    expiration time every contract has obtained the requisite number of user
    visits.

  4. Bidding for Representative Allocations for Display Advertising.

    Ср, 10/07/2009 - 12:26 — SomNambul
    Authors: Sergei Vassilvitskii, Arpita Ghosh, Preston McAfee, Kishore Papineni
    Subjects: Multiagent Systems
    Abstract

    Display advertising has traditionally been sold via guaranteed contracts -- a
    guaranteed contract is a deal between a publisher and an advertiser to allocate
    a certain number of impressions over a certain period, for a pre-specified
    price per impression. However, as spot markets for display ads, such as the
    RightMedia Exchange, have grown in prominence, the selection of advertisements
    to show on a given page is increasingly being chosen based on price, using an
    auction. As the number of participants in the exchange grows, the price of an
    impressions becomes a signal of its value.

  5. Social Networks and Stable Matchings in the Job Market.

    Ср, 10/07/2009 - 12:26 — SomNambul
    Authors: Esteban Arcaute, Sergei Vassilvitskii
    Subjects: Computer Science and Game Theory
    Abstract

    For most people, social contacts play an integral part in finding a new job.
    As observed by Granovetter's seminal study, the proportion of jobs obtained
    through social contacts is usually large compared to those obtained through
    postings or agencies. At the same time, job markets are a natural example of
    two-sided matching markets. An important solution concept in such markets is
    that of stable matchings, and the use of the celebrated Gale-Shapley algorithm
    to compute them.

Math-arch

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