Statistics for high-dimensional data

By Theofanis Sapatinas. 2012

109 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “In a certain sense the the work presented here is the SVM counterpart of recent work by Bühlmann and Van De Geer (2011) in addressing the statistical inference problem for L1-regularized high dimensional regression methods (related to the LASSO).”
Parallel Selective Algorithms for Nonconvex Big Data Optimization (2014, unknown): “It is hard here to even summarize the huge amount of work done in this field; we refer the reader to the recent works [2]–[14] and books [15]–[17] as entry points to the literature.”
Generalized Sobol sensitivity indices for dependent variables: numerical methods (2013, unknown): “[31] The main advantage of the modified LARS algorithm is that it builds up the whole regularized solution path {β̂(λ), λ ∈ R}, exploiting the property of piecewise linearity of the solutions with respect to λ.[28,32] Moreover, the modified LARS yields all lasso solutions, making this algorithm theo”
An Introduction to Matrix Concentration Inequalities (2015, unknown): “Random matrix theory plays a key role in this field [123, 133, 101, 31].”
Encoding of mixtures in a simple olfactory system. (2013, unknown): “We used the multi-stage adaptive lasso (Buhlmann, 2011) to regress the binned odor responses of KCs on those of the PNs, and found that by using up to 30 of the available 174 PNs, we could explain ~50% of the variance in KC responses on average.”