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Travelled to:
1 × Canada
1 × France
1 × Germany
1 × Korea
1 × Spain
1 × United Kingdom
3 × USA
Collaborated with:
M.W.Mahoney Y.Makris C.Boutsidis M.Maggioni S.Almukhaizim K.Akcoglu M.Kao I.Kerenidis P.Raghavan M.Magdon-Ismail D.P.Woodruff A.Dasgupta B.Harb V.Josifovski
Talks about:
concurr (2) tensor (2) select (2) featur (2) detect (2) error (2) fsms (2) fast (2) data (2) unsupervis (1)

Person: Petros Drineas

DBLP DBLP: Drineas:Petros

Contributed to:

ICML 20122012
KDD 20082008
KDD 20072007
KDD 20062006
VLDB 20062006
DATE v1 20042004
DATE 20032003
ICALP 20022002
STOC 20022002

Wrote 9 papers:

ICML-2012-MahoneyDMW #approximate #matrix #performance #statistics
Fast approximation of matrix coherence and statistical leverage (MWM, PD, MMI, DPW), p. 137.
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.
DATE-v1-2004-AlmukhaizimDM #bound #concurrent #detection #fault #latency #on the
On Concurrent Error Detection with Bounded Latency in FSMs (SA, PD, YM), pp. 596–603.
DATE-2003-DrineasM #concurrent #detection #fault #monitoring
Non-Intrusive Concurrent Error Detection in FSMs through State/Output Compaction and Monitoring via Parity Trees (PD, YM), pp. 11164–11167.
ICALP-2002-AkcogluDK #performance
Fast Universalization of Investment Strategies with Provably Good Relative Returns (KA, PD, MYK), pp. 888–900.
STOC-2002-DrineasKR #recommendation
Competitive recommendation systems (PD, IK, PR), pp. 82–90.

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.