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: Mahoney:Michael_W=
Contributed to:
2015
201420142013
20132012
20122011200820072006
2006Wrote 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.
- KDD-2006-MahoneyMD
- 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.