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Found 7 papers, 3 papers with code
Structure Learning of Gaussian Markov Random Fields with False Discovery Rate Control
Adapting SL1 for probabilistic graphical models, we show that SL1 can be used for the structure learning of Gaussian MRFs using our suggested procedure nsSLOPE (neighborhood selection Sorted L-One Penalized Estimation), controlling the FDR of detecting edges.
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates.
Methodology Applications Computation
High-dimensional robust regression and outliers detection with SLOPE
Following a recent set of works providing methods for simultaneous robust regression and outliers detection, we consider in this paper a model of linear regression with individual intercepts, in a high-dimensional setting.
Sparse Portfolio Selection via the sorted $\ell_{1}$-Norm
We introduce a financial portfolio optimization framework that allows us to automatically select the relevant assets and estimate their weights by relying on a sorted $\ell_1$-Norm penalization, henceforth SLOPE.
Group SLOPE - adaptive selection of groups of predictors
Sorted L-One Penalized Estimation (SLOPE) is a relatively new convex optimization procedure which allows for adaptive selection of regressors under sparse high dimensional designs.
Methodology 46N10 G.1.6
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered l1-Norm
In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-point (SP) reformulation.
False Discoveries Occur Early on the Lasso Path
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any.
