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Found 8 papers, 1 papers with code
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices.
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
To efficiently estimate the hub graphical models, we introduce a two-phase algorithm.
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
In particular, we show how specific DPPs and a string of controlled approximations can lead to gradient estimators with a variance that decays faster with the batchsize than under uniform sampling.
Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets
In this work, we investigate spectrogram analysis via an examination of the stochastic geometric properties of their level sets.
Estimation of sparse Gaussian graphical models with hidden clustering structure
The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities.
Efficient algorithms for multivariate shape-constrained convex regression problems
We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables and at least $n(n-1)$ linear inequality constraints, where $n$ is the number of data points.
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
In addition, we derive the corresponding HS-Jacobian to the proximal mapping and analyze its structure --- which plays an essential role in the efficient computation of the PPA subproblem via applying a semismooth Newton method on its dual.
Efficient sparse semismooth Newton methods for the clustered lasso problem
Based on the new formulation, we derive an efficient procedure for its computation.
