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Michael Rabbat

  1. GANC: Greedy Agglomerative Normalized Cut.

    Fri, 05/06/2011 - 15:01 — SomNambul
    Authors: Mark Coates, Michael Rabbat, Seyed Salim Tabatabaei
    Subjects: Artificial Intelligence
    Abstract

    This paper describes a graph clustering algorithm that aims to minimize the
    normalized cut criterion and has a model order selection procedure. The
    performance of the proposed algorithm is comparable to spectral approaches in
    terms of minimizing normalized cut. However, unlike spectral approaches, the
    proposed algorithm scales to graphs with millions of nodes and edges. The
    algorithm consists of three components that are processed sequentially: a
    greedy agglomerative hierarchical clustering procedure, model order selection,
    and a local refinement.

  2. Large scale probabilistic available bandwidth estimation.

    Tue, 07/06/2010 - 15:03 — SomNambul
    Authors: Mark Coates, Michael Rabbat, Frederic Thouin
    Subjects: Networking and Internet Architecture
    Abstract

    The common utilization-based definition of available bandwidth and many of
    the existing tools to estimate it suffer from several important weaknesses: i)
    most tools report a point estimate of average available bandwidth over a
    measurement interval and do not provide a confidence interval; ii) the commonly
    adopted models used to relate the available bandwidth metric to the measured
    data are invalid in almost all practical scenarios; iii) existing tools do not
    scale well and are not suited to the task of multi-path estimation in
    large-scale networks; iv) almost all tools use ad-hoc techniques

  3. Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning.

    Fri, 01/08/2010 - 16:01 — SomNambul
    Authors: Mark Coates, Michael Rabbat, Frederic Thouin
    Subjects: Networking and Internet Architecture
    Abstract

    Knowing the largest rate at which data can be sent on an end-to-end path such
    that the egress rate is equal to the ingress rate with high probability can be
    very practical when choosing transmission rates in video streaming or selecting
    peers in peer-to-peer applications. We introduce probabilistic available
    bandwidth, which is defined in terms of ingress rates and egress rates of
    traffic on a path, rather than in terms of capacity and utilization of the
    constituent links of the path like the standard available bandwidth metric.

  4. Greedy Gossip with Eavesdropping.

    Fri, 09/11/2009 - 19:13 — SomNambul
    Authors: Deniz Ustebay, Boris Oreshkin, Mark Coates, Michael Rabbat
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    This paper presents greedy gossip with eavesdropping (GGE), a novel
    randomized gossip algorithm for distributed computation of the average
    consensus problem. In gossip algorithms, nodes in the network randomly
    communicate with their neighbors and exchange information iteratively. The
    algorithms are simple and decentralized, making them attractive for wireless
    network applications. In general, gossip algorithms are robust to unreliable
    wireless conditions and time varying network topologies. In this paper we
    introduce GGE and demonstrate that greedy updates lead to rapid convergence.

Math-arch

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