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Block Splitting for Large-Scale Distributed Learning
Block Splitting Large-Scale Distributed Learning
2015/7/9
Machine learning and statistics with very large datasets is now a topic of widespread interest, both in academia and industry. Many such tasks can be posed as convex optimization problems, so algorith...
Block Splitting for Distributed Optimization
Distributed optimization · Alternating direction method of multipliers Operator splitting Proximal operators Cone programming Machine learning
2015/7/9
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
Stable and efficient updates to the basis matrix factors are vital to the simplex
method. The "best" updating method depends on the machine in use and how the update is implemented. For example, the ...
Professor HENRY W. BLOCK,Department of Statistics at University of Pittsburgh（图）
Professor HENRY W. BLOCK Department of Statistics at University of Pittsburgh Reliability theory Positive and negative dependence concepts used in multiple testing Distribution theory
2014/3/26
HENRY W. BLOCK，PROFESSOR/GRADUATE DIRECTOR，2703 Cathedral of Learning，“The importance of statistics is well known in almost all academic disciplines and also to the students who are required to take a...
Adapting the Stochastic Block Model to Edge-Weighted Networks
Adapting Stochastic Block Model Edge-Weighted Networks
2013/6/14
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduces several technical...
On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
Randomized block-coordinate descent accelerated coordinate descent iteration complexity convergence rate composite minimization
2013/6/17
In this paper we analyze the randomized block-coordinate descent (RBCD) methods proposed in [8,11] for minimizing the sum of a smooth convex function and a block-separable convex function. In particul...
An analysis of block sampling strategies in compressed sensing
Compressed Sensing blocks of measurements sampling continuous trajectories exact recovery,ℓ 1 minimization.
2013/6/17
Compressed sensing (CS) is a theory which guarantees the exact recovery of sparse signals from a few number of linear projections. The sampling schemes suggested by current CS theories are often of li...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Block Thresholding on the Sphere
Block Thresholding Needlets Spherical Data Nonpara-metric Regression
2013/4/27
Th aim of this paper is to study the nonparametric regression estimators on the sphere built by the needlet block thresholding. The block thresholding procedure proposed here follows the method introd...
Unified Analysis of Transmit Antenna Selection/Space-Time Block Coding with Receive Selection and Combining over Nakagami-m Fading Channels in the Presence of Feedback Errors
Space-Time Block Coding (STBC) Transmit Antenna Selection (TAS) Receive Antenna Selection (RAS) Maximal-ratio Combining (MRC) Selection Combining (SC) Nakagami-m fading Feedback Errors
2012/9/18
Examining the effect of imperfect transmit antenna selection (TAS) caused by the feedback link errors on the performance of hybrid TAS/space-time block co ding (STBC) with selection combining (SC) (i....
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function
Block coordinate descent iteration complexity composite minimization
2011/7/19
In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function and prove that it obtains an $\epsilon$-...
Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model
maximum-likelihood Stochastic Block Model
2011/6/17
The stochastic block model (SBM) is a probabilistic model de-
signed to describe heterogeneous directed and undirected graphs. In this
paper, we address the asymptotic inference on SBM by use of max...
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
Multiple Measurement Vectors Block Sparsity Time-Varying Sparsity
2011/6/16
A trend in compressed sensing (CS) is to exploit struc-
ture for improved reconstruction performance. In the
basic CS model (i.e. the single measurement vec-
tor model), exploiting the clustering s...
Regenerative block empirical likelihood for Markov chains
Nummelin splitting technique time series Empirical Likelihood
2011/3/21
Empirical likelihood is a powerful semi-parametric method increasingly investigated in the literature. However, most authors essentially focus on an i.i.d. setting. In the case of dependent data, the ...
Regenerative block empirical likelihood for Markov chains
Nummelin splitting technique time series Empirical Likelihood MSC codes: 62G05 62F35 62F40
2011/3/23
Empirical likelihood is a powerful semi-parametric method increasingly investigated in the literature. However, most authors essentially focus on an i.i.d. setting. In the case of dependent data, the ...