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Found 5 papers, 2 papers with code
Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor.
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.
Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems
Even though the use of $\ell_1$-based sparse coding for model compression is not new, we show that it can be far more effective than previously reported when we use proximal point algorithms and the technique of debiasing.
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.
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.
