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- PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary ... MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic...
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- smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness ... estimator for high dimensional but sparse additive models. Finally, an adaptive version of our sparsity...
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- address the problem of sparse selection in linear models. A number of nonconvex penalties have...
- Referenced in 747 articles
- development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments ... geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics...
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- variety of convex models, including in particular those employed in sparse recovery applications...
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- Lasso for Hierarchical Interactions. Fits sparse interaction models for continuous and binary responses subject...
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- Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method ... adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension ... these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either ... computation is memory-optimized using the sparse matrix output...
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- recently proposed data-driven modelling framework that uses sparse regression techniques to identify nonlinear ... identified models compare favourably against reduced-order models obtained from a standard Galerkin projection procedure ... entire code base for our constrained sparse Galerkin regression algorithm is freely available online...
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- versatile collection of Gaussian process models and computational tools required for inference. The tools include ... among others, various inference methods, sparse approximations and model assessment methods. The GPstuff toolbox works...
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- SVMlight can also train SVMs with cost models (see [Morik et al., 1999]). The code ... applications. Many tasks have the property of sparse instance vectors. This implementation makes...
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- Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse ... Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination ... computation is memory-optimized using the sparse matrix output, and accelerated by the path following...
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- HiLasso: a collaborative hierarchical sparse modeling framework. Sparse modeling is a powerful framework for data ... sparsity property of the Group Lasso, where sparse groups of features are jointly encoded, obtaining ... group) level, obtaining the collaborative HiLasso model (C-HiLasso). Such signals then share the same...
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- solving large-scale nonlinear programs involving sparse nonlinear constraints. The paper will discuss strategic ... contains performance statistics for a range of models from different branches of engineering and economics...
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- Bayesian structure learning in sparse Gaussian graphical models. Decoding complex relationships among large numbers...
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- Significance testing in non-sparse high-dimensional linear models. In high-dimensional linear models ... error approaching zero for sparse and many dense models. In fact, CorrT is also shown ... frameworks: sparse, non-sparse and hybrid models where sparse and dense signals are mixed. Numerical...
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- which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso...
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- linear model fitting functions that implement least squares methods for problems with sparse design matrices...
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- modeling approach supported by modern AML (algebraic modeling language) tools. Pyomo is a flexible, extensible ... orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which ... which makes it easy to create Pyomo models, apply a variety of optimizers, and examine...
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- Bayesian Estimation and Prediction in Sparse Additive Models. This package is intended to perform estimation ... prediction in high-dimensional additive models, using a sparse PAC-Bayesian point of view ... Bayesian Estimation and Prediction in Sparse Additive Models’, Electronic Journal of Statistics...
- Referenced in 33 articles
- model-checking algorithms, are available. For the study of stochastic and timing behavior, both sparse ... process, but certain classes of non-Markov models can still be solved numerically. Finally, since...