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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7164
high-dimensional scaling joint support recovery sufficient condition sharp set standard gaussian ensemble exact variable selection second set model dimension regression coefficient share linear regression problem general gaussian matrix result applies phase transition close agreement rescaled sample size support converges regularized regression ...
https://pdfs.semanticscholar.org/b7ad/fb505bf77f5e1066bf9cb638dea422fd7a25.pdf
Joint support recovery under high-dimensional scaling: Benefits and perils of `1,∞-regularization Sahand Negahban Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1770 [email protected] Martin J. Wainwright
https://www.semanticscholar.org/paper/Joint-support-recovery-under-high-dimensional-and-%E2%84%93-Negahban-Wainwright/0c2aea1b3b7ccd49f7f7b37416e3cb089554593f
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization @inproceedings{Negahban2008JointSR, title={Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization}, author={Sahand N. Negahban and Martin J. Wainwright}, booktitle={NIPS 2008}, year={2008} } ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.565.1802
high-dimensional scaling joint support recovery sufficient condition sharp set standard gaussian ensemble exact variable selection second set model dimension regression coefficient share support converges general gaussian matrix result applies phase transition close agreement linear regression problem regularization yield minimal sample size ...
https://core.ac.uk/display/21057329
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ1,∞-regularization ... This set-up suggests the use of ℓ1/ℓ∞-regularized regression for joint estimation of the p × r matrix of regression coefficients. We analyze the high-dimensional scaling of ℓ1/ℓ∞-regularized quadratic programming, considering ...
https://people.eecs.berkeley.edu/~wainwrig/Papers/OboWaiJor11.pdf
block regularization based on the 1/ 2 norm is used for support union re-covery, or recovery of the set of s rows for which B∗ is nonzero. Under high-dimensional scaling, we show that the multivariate group Lasso exhibits a threshold for the recovery of the exact row pattern with high probability over
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7164
high-dimensional scaling joint support recovery sufficient condition sharp set standard gaussian ensemble exact variable selection second set model dimension regression coefficient share linear regression problem general gaussian matrix result applies phase transition close agreement rescaled sample size support converges regularized regression ...
https://pdfs.semanticscholar.org/b7ad/fb505bf77f5e1066bf9cb638dea422fd7a25.pdf
Joint support recovery under high-dimensional scaling: Benefits and perils of `1,∞-regularization Sahand Negahban Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1770 [email protected] Martin J. Wainwright
https://www.semanticscholar.org/paper/Joint-support-recovery-under-high-dimensional-and-%E2%84%93-Negahban-Wainwright/0c2aea1b3b7ccd49f7f7b37416e3cb089554593f
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization @inproceedings{Negahban2008JointSR, title={Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization}, author={Sahand N. Negahban and Martin J. Wainwright}, booktitle={NIPS 2008}, year={2008} } ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.565.1802
high-dimensional scaling joint support recovery sufficient condition sharp set standard gaussian ensemble exact variable selection second set model dimension regression coefficient share support converges general gaussian matrix result applies phase transition close agreement linear regression problem regularization yield minimal sample size ...
https://projecteuclid.org/download/pdfview_1/euclid.aos/1291388368
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1 ∕ ℓ ∞ regularization. In Advances in Neural Information Processing Systems 21 1161–1168. MIT Press, Cambridge, MA. Obozinski, G., Taskar, B. and Jordan, M. I. (2010). Joint covariate selection and joint subspace selection for multiple classification ...Cited by: 322
https://core.ac.uk/display/101631672
We analyze the high-dimensional scaling of `1/`∞-regularized quadratic program-ming, considering both consistency rates in `∞-norm, and also how the minimal sample size n required for performing variable selection grows as a function of the model dimension, sparsity, and overlap between the supports.Author: Sahand Negahban and Martin J. Wainwright
https://core.ac.uk/display/21057329
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ1,∞-regularization ... This set-up suggests the use of ℓ1/ℓ∞-regularized regression for joint estimation of the p × r matrix of regression coefficients. We analyze the high-dimensional scaling of ℓ1/ℓ∞-regularized quadratic programming, considering ...
https://www.researchgate.net/publication/240992089_Simultaneous_support_recovery_in_high_dimensions_Benefits_and_perils_of_block_l1linfinity-regularization
Under high-dimensional scaling, we show that the multivariate group Lasso exhibits a threshold for the recovery of the exact row pattern with high probability over the random design and noise that ...
http://www.stat.yale.edu/~snn7/
Joint support recovery under high-dimensional scaling: Benefits and perils of $\ell_{1,\infty}$-regularization. S. Negahban and M. J. Wainwright. Advances in Neural Information Processing Systems, December 2008. Vancouver, Canada. Journal version:
https://www.researchgate.net/publication/224392863_High-dimensional_union_support_recovery_in_multivariate
High-dimensional union support recovery in multivariate ... Studying this problem under high-dimensional scaling, we show that group Lasso recovers the exact row pattern with high probability over ...
https://www.researchgate.net/publication/224392863_High-dimensional_union_support_recovery_in_multivariate
High-dimensional union support recovery in multivariate ... Studying this problem under high-dimensional scaling, we show that group Lasso recovers the exact row pattern with high …
https://lu.seas.harvard.edu/files/yuelu/files/wang_2019_j._stat._mech._2019_124011.pdf
The scaling limit of high-dimensional online independent component analysis* ... and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates ... sparse support recovery using a simple hard-thresholding scheme on the estimates
https://iopscience.iop.org/article/10.1088/1742-5468/ab39d6
The scaling limit of high-dimensional online independent ... as the ambient dimension and with proper time-scaling, the time-varying joint empirical measure of the true underlying independent component and its estimate converges ... we show in figure 3 the performance of sparse support recovery using a simple hard-thresholding scheme on the ...
https://dl.acm.org/doi/10.5555/2981780.2981932
Home Conferences NIPS Proceedings NIPS'08 High-dimensional support union recovery in multivariate regression. Article . High-dimensional support union recovery in multivariate regression. Share on. Authors: Guillaume Obozinski. Department of Statistics, UC Berkeley.
https://www.ima.umn.edu/2017-2018.6/W2.21-23.18/26795
We investigate the identifiability of such models, as well as estimation and inference issues under high-dimensional scaling. The performance of the proposed methods is assessed through synthetic data and the methodology is illustrated on a economic data set. This talk is based on joint work with Jiahe Lin.
https://ieeexplore.ieee.org/document/6696708/media
Fast redundancy resolution for high-dimensional robots executing prioritized tasks under hard bounds in the joint space Abstract: A kinematically redundant robot with limited motion capabilities, expressed by inequality constraints of the box type on joint variables and commands, needs to perform a set of tasks, expressed by linear equality ...
https://papers.nips.cc/paper/7241-the-scaling-limit-of-high-dimensional-online-independent-component-analysis.pdf
The Scaling Limit of High-Dimensional Online Independent Component Analysis ... We analyze the dynamics of an online algorithm for independent component analysis in the high-dimensional scaling limit. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical ... time-scaling, the ...
https://arxiv.org/abs/1710.05384v1
We analyze the dynamics of an online algorithm for independent component analysis in the high-dimensional scaling limit. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates provided by the algorithm will converge weakly to a deterministic measured-valued process that can ...
https://people.eecs.berkeley.edu/~wainwrig/Papers/NegWai11_FullVersion.pdf
ESTIMATION OF (NEAR) LOW-RANK MATRICES WITH NOISE AND HIGH-DIMENSIONAL SCALING BY SAHAND NEGAHBAN ANDMARTINJ. WAINWRIGHT1,2 University of California, Berkeley We study an instance of high-dimensional inference in which the goal is to estimate a matrix ∗ ∈ Rm1×m2 on the basis of N noisy observa-tions.
https://projecteuclid.org/download/pdfview_1/euclid.aos/1572487389
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices Bunea, Florentina, She, Yiyuan, and Wegkamp, Marten H., The Annals of Statistics, 2012; Optimal selection of reduced rank estimators of high-dimensional matrices Bunea, Florentina, She, Yiyuan, and Wegkamp, Marten H., The Annals of Statistics, 2011
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