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Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter
Travelled to:
1 × Australia
1 × Korea
1 × United Kingdom
2 × China
7 × USA
Collaborated with:
P.Drineas X.Meng J.Yang D.F.Gleich D.Gleich A.Gittens L.Orecchia P.Ma B.Yu C.Boutsidis M.Maggioni V.Sindhwani H.Avron M.Magdon-Ismail D.P.Woodruff A.Dasgupta B.Harb V.Josifovski
Talks about:
algorithm (4) approxim (4) regress (3) method (3) featur (3) scale (3) larg (3) data (3) spectral (2) implicit (2)

Person: Michael W. Mahoney

DBLP DBLP: Mahoney:Michael_W=

Contributed to:

KDD 20152015
ICML c1 20142014
ICML c2 20142014
ICML c3 20132013
STOC 20132013
ICML 20122012
PODS 20122012
ICML 20112011
KDD 20082008
KDD 20072007
KDD 20062006
VLDB 20062006

Wrote 15 papers:

KDD-2015-GleichM #algorithm #graph #learning #using
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms (DFG, MWM), pp. 359–368.
ICML-c1-2014-MaMY #algorithm #statistics
A Statistical Perspective on Algorithmic Leveraging (PM, MWM, BY), pp. 91–99.
ICML-c1-2014-YangSAM #invariant #kernel #monte carlo
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels (JY, VS, HA, MWM), pp. 485–493.
ICML-c2-2014-GleichM #algorithm #approximate #case study
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow (DG, MWM), pp. 1018–1025.
ICML-c3-2013-GittensM #machine learning #scalability
Revisiting the Nystrom method for improved large-scale machine learning (AG, MWM), pp. 567–575.
ICML-c3-2013-MengM #pipes and filters #robust
Robust Regression on MapReduce (XM, MWM), pp. 888–896.
ICML-c3-2013-YangMM #scalability
Quantile Regression for Large-scale Applications (JY, XM, MWM), pp. 881–887.
STOC-2013-MengM #linear #robust
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression (XM, MWM), pp. 91–100.
ICML-2012-MahoneyDMW #approximate #matrix #performance #statistics
Fast approximation of matrix coherence and statistical leverage (MWM, PD, MMI, DPW), p. 137.
PODS-2012-Mahoney #approximate #data analysis #scalability
Approximate computation and implicit regularization for very large-scale data analysis (MWM), pp. 143–154.
ICML-2011-MahoneyO #approximate #implementation
Implementing regularization implicitly via approximate eigenvector computation (MWM, LO), pp. 121–128.
KDD-2008-BoutsidisMD #analysis #component #feature model
Unsupervised feature selection for principal components analysis (CB, MWM, PD), pp. 61–69.
KDD-2007-DasguptaDHJM #classification #feature model
Feature selection methods for text classification (AD, PD, BH, VJ, MWM), pp. 230–239.
Tensor-CUR decompositions for tensor-based data (MWM, MM, PD), pp. 327–336.
VLDB-2006-DrineasM #algorithm #matrix #random #set
Randomized Algorithms for Matrices and Massive Data Sets (PD, MWM), p. 1269.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.