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High-dimensional statistics
Results: 273

#	Item
21	SHIV: Reducing Supervisor Burden in DAgger using Support Vectors for Efficient Learning from Demonstrations in High Dimensional State Spaces Michael Laskey1 , Sam Staszak1 , Wesley Yu-Shu Hsieh1 , Jeffrey Mahler1 , Flori Add to Reading List Source URL: goldberg.berkeley.edu Language: English - Date: 2016-02-18 10:52:35 Machine learning Artificial intelligence Statistics Learning Statistical classification Support vector machine Apprenticeship learning Artificial neural network Loss function Motion planning Reinforcement learning Robotics
22	On Iterative Hard Thresholding Methods for High-dimensional M-Estimation arXiv:1410.5137v2 [cs.LG] 21 OctPrateek Jain∗ Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-10-21 20:16:25 Statistics Estimation theory Statistical theory Consistent estimator M-estimator Loss function Linear regression Sufficient statistic
23	High-dimensional statistics: Some progress and challenges ahead Add to Reading List Source URL: events.csml.ucl.ac.uk Language: English - Date: 2013-05-02 09:18:39
24	On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain∗ Ambuj Tewari† Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2015-01-11 15:04:36 Statistics Statistical theory Estimation theory Sparse approximation Bias of an estimator Consistent estimator Loss function M-estimator Linear regression Sufficient statistic
25	Estimation in High-dimensional Vector Autoregressive Models with Noisy Data Kam Chung Wong1 and Ambuj Tewari2 1 Department of Statistics, University of Michigan, Ann Arbor Add to Reading List Source URL: ctools.umich.edu Language: English Time series models Signal processing Econometrics Noise Vector autoregression Autoregressive model Value at risk Time series Vector
26	High-dimensional statistics: Some progress and challenges ahead Add to Reading List Source URL: events.csml.ucl.ac.uk Language: English - Date: 2013-05-02 09:18:39
27	AWARDS & ANNOUNCEMENTS Dr. Lee Dicker Dense and Sparse Methods in High-Dimensional Data Analysis Many statistical methods for high-dimensional data analysis begin with the assumption that the parameter of interest is, i Add to Reading List Source URL: statistics.rutgers.edu Language: English - Date: 2013-06-07 14:30:04
28	Submitted to the Annals of Statistics GAUSSIAN APPROXIMATIONS AND MULTIPLIER BOOTSTRAP FOR MAXIMA OF SUMS OF HIGH-DIMENSIONAL RANDOM VECTORS∗ By Victor Chernozhukov† , Denis Chetverikov‡ and Kengo Add to Reading List Source URL: www.econ.ucla.edu Language: English - Date: 2015-04-06 16:37:10
29	High-dimensional data, random questions and random answers Sara van de Geer Statistics is crucial for dealing with the large amount of data available today. There are many machine learning algorithms around that help us Add to Reading List Source URL: www.math.leidenuniv.nl Language: English - Date: 2015-03-24 04:59:21 Randomness Mathematics Theoretical computer science Algorithm Mathematical logic
30	Evaluating Subspace Clustering Algorithms Lance Parsons Ehtesham Haque Add to Reading List Source URL: www.public.asu.edu Language: English - Date: 2004-04-19 16:42:54 K-means clustering Medoid Principal component analysis Consensus clustering Statistics Cluster analysis Clustering high-dimensional data

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