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149
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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be interpreted as recursive ℓ1norm penalized regression. Our second algorithm, based on Nesterov’s first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright
DECOrrelated feature space partitioning for distributed sparse regression
"... Abstract Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for downscaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be part ..."
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correlations or inefficient in reducing the model dimension. In this paper, we solve these problems through a new embarrassingly parallel framework named DECO for distributed variable selection and parameter estimation. In DECO, variables are first partitioned and allocated to m distributed workers
Recursive Binary Time Partitioning For LowPower Mobile Discovery and Bandwidth Allocation in CloudBased Wireless LiveStreaming
"... Abstract Now a day’s increases the WiFi and Bluetooth capability with increasing prevalence of mobile wireless devices. New applications are emerging that can make use of limited contact opportunities when the devices are physically close. Discovering such services is a challenging problem. It is ..."
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. Number of neighbor discovery schemes existing that, assume lack of any time synchronization. In practice sufficiently accurate time synchronization can be achieved with existing time synchronization techniques. New scheme is introduced Recursive Binary Time Partitioning (RBTP), that determines how
Anglebased space partitioning for efficient parallel skyline computation
 In SIGMOD Conference
, 2008
"... Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such gridbased par ..."
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Cited by 28 (6 self)
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Recently, skyline queries have attracted much attention in the database research community. Space partitioning techniques, such as recursive division of the data space, have been used for skyline query processing in centralized, parallel and distributed settings. Unfortunately, such grid
Largescale multirobot task allocation via dynamic partitioning and distribution. Autonomous Robots 33(3):291–307
, 2012
"... This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and ..."
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Cited by 7 (3 self)
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and unnecessary repeated computation. An allocation results by operating on each partition: either the steps are repeated recursively to refine the generalized assignment, or each subproblem may be solved by an existing algorithm. The results suggest that only a minor sacrifice in solution quality is needed
MultiLevel Partitioning and Distribution of the Assignment Problem for LargeScale MultiRobot Task Allocation
"... Abstract — A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentral ..."
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Cited by 8 (1 self)
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be computed through coarsening and partitioning operations on the utility matrix. First, a coarse assignment is calculated by evaluating the global utility information and partitioning it into clusters in a problemdomain independent way. Next, the assignment solutions in each partition are refined (either
Beta Regression: Shaken, Stirred, Mixed, and Partitioned
"... beta regression, finite mixture, proportion, rate, modelbased recursive partitioning The class of beta regression models is commonly used by practitioners to model variables that assume values in the open standard unit interval (0, 1). It is based on the assumption that the dependent variable is be ..."
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beta regression, finite mixture, proportion, rate, modelbased recursive partitioning The class of beta regression models is commonly used by practitioners to model variables that assume values in the open standard unit interval (0, 1). It is based on the assumption that the dependent variable
Scheduling Divisible Workloads from Multiple Sources in Linear Daisy Chain Networks
"... Abstract — This paper considers scheduling divisible workloads from multiple sources in linear networks of processors. We propose a two phase scheduling strategy (TPSS) to minimize the overall processing time of these workloads by taking advantage of the processor equivalence technique. A case study ..."
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of the overall workloads. In the second phase, we propose an efficient algorithm to obtain nearoptimal load distribution among processors represented by the equivalent processor. Experimental evaluation through simulations demonstrate performance improvement using our schemes compared to the equal partition
Marvin: distributed reasoning over largescale Semantic Web data
, 2009
"... Many Semantic Web problems are difficult to solve through common divideandconquer strategies, since they are hard to partition. We present Marvin, a parallel and distributed platform for processing large amounts of RDF data, on a network of looselycoupled peers. We present our divideconquerswap ..."
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Cited by 22 (3 self)
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Many Semantic Web problems are difficult to solve through common divideandconquer strategies, since they are hard to partition. We present Marvin, a parallel and distributed platform for processing large amounts of RDF data, on a network of looselycoupled peers. We present our divide
Efficient Query Processing on Relational DataPartitioning Index Structures
"... In contrast to spacepartitioning index structures, datapartitioning index structures naturally adapt to the actual data distribution which results in a very good query response behavior. Besides efficient query processing, modern database applications including computeraided design, medical imagi ..."
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In contrast to spacepartitioning index structures, datapartitioning index structures naturally adapt to the actual data distribution which results in a very good query response behavior. Besides efficient query processing, modern database applications including computeraided design, medical
Results 1  10
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149