Alan Willsky
The Hierarchical Dirichlet Process Hidden Semi-Markov Model.
Mon, 03/19/2012 - 15:01 — SomNambulAuthors: Alan Willsky, Matthew J. Johnson
Subjects: Artificial Intelligence
AbstractThere is much interest in the Hierarchical Dirichlet Process Hidden Markov
Model (HDP-HMM) as a natural Bayesian nonparametric extension of the
traditional HMM. However, in many settings the HDP-HMM's strict Markovian
constraints are undesirable, particularly if we wish to learn or encode
non-geometric state durations.Energy-Latency Tradeoff for In-Network Function Computation in Random Networks.
Thu, 01/06/2011 - 16:01 — SomNambulAuthors: Animashree Anandkumar, Alan Willsky, Paul Balister, Béla Bollobás
Subjects: Networking and Internet Architecture
AbstractThe problem of designing policies for in-network function computation with
minimum energy consumption subject to a latency constraint is considered. The
scaling behavior of the energy consumption under the latency constraint is
analyzed for random networks, where the nodes are uniformly placed in growing
regions and the number of nodes goes to infinity. The special case of sum
function computation and its delivery to a designated root node is considered
first.High Dimensional Structure Learning of Ising Models on Sparse Random Graphs.
Tue, 11/02/2010 - 16:01 — SomNambulAuthors: Animashree Anandkumar, Alan Willsky, Vincent Tan
Subjects: Statistics
AbstractWe consider the problem of learning the structure of ferromagnetic Ising
models Markov on sparse Erdos-Renyi random graph. We propose simple local
algorithms and analyze their performance in the regime of correlation decay. We
prove that an algorithm based on a set of conditional mutual information tests
is consistent for structure learning throughout the regime of correlation
decay. This algorithm requires the number of samples to scale as \omega(\log
n), and has a computational complexity of O(n^5).Sequential Compressed Sensing.
Tue, 03/02/2010 - 16:01 — SomNambulAuthors: Dmitry Malioutov, Sujay Sanghavi, Alan Willsky
Subjects: Information Theory
AbstractCompressed sensing allows perfect recovery of sparse signals (or signals
sparse in some basis) using only a small number of random measurements.
Existing results in compressed sensing literature have focused on
characterizing the achievable performance by bounding the number of samples
required for a given level of signal sparsity.
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