English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Regularized m-estimators are widely used due to their ability of recovering alow-dimensional model in high-dimensional scenarios. Some recent efforts onthis subject focused on creating a unified framework for establishing oraclebounds, and deriving conditions for support recovery. Under this sameframework, we propose a new Generalized Information Criteria (GIC) that takesinto consideration the sparsity pattern one wishes to recover. We obtainnon-asymptotic model selection bounds and sufficient conditions for modelselection consistency of the GIC. Furthermore, we show that the GIC can also beused for selecting the regularization parameter within a regularized$m$-estimation framework, which allows practical use of the GIC for modelselection in high-dimensional scenarios. We provide examples of group LASSO inthe context of generalized linear regression and low rank matrix regression.

Generalized Information Criteria for Structured Sparse Models