BibSLEIGH — Shai_Shalev-Shwartz

Travelled to:

1 × France
1 × Israel
1 × United Kingdom
2 × Canada
2 × China
2 × Finland
5 × USA

Collaborated with:

Y.Singer N.Srebro A.Gonen A.Tewari A.Daniely A.Cotter O.Shamir T.Zhang A.Vinnikov A.Globerson N.Linial N.Srebro S.Sabato R.Urner S.Ben-David N.Cesa-Bianchi S.M.Kakade A.Y.Ng E.Eban A.Birnbaum J.C.Duchi T.Chandra M.Fink S.Ullman S.Dubnov N.Friedman R.Livni D.Lehavi S.Schein H.Nachlieli

Talks about:

learn (9) onlin (5) effici (4) stochast (3) predict (3) minim (3) multiclass (2) regular (2) complex (2) compon (2)

Person: Shai Shalev-Shwartz

DBLP: Shalev-Shwartz:Shai

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2004

2002

Wrote 20 papers:

ICML-2015-DanielyGS #adaptation #learning #online: Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICML-c1-2014-Shalev-Shwartz0 #coordination #probability: Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization (SSS, TZ), pp. 64–72.
ICML-c2-2014-VinnikovS #component #independence: K-means recovers ICA filters when independent components are sparse (AV, SSS), pp. 712–720.
STOC-2014-DanielyLS #complexity #learning: From average case complexity to improper learning complexity (AD, NL, SSS), pp. 441–448.
ICML-c1-2013-CotterSS #learning: Learning Optimally Sparse Support Vector Machines (AC, SSS, NS), pp. 266–274.
ICML-c1-2013-GonenSS #approach #learning #performance: Efficient Active Learning of Halfspaces: an Aggressive Approach (AG, SS, SSS), pp. 480–488.
ICML-c1-2013-LivniLSNSG #analysis #component: Vanishing Component Analysis (RL, DL, SS, HN, SSS, AG), pp. 597–605.
ICML-2012-CotterSS #kernel #probability: The Kernelized Stochastic Batch Perceptron (AC, SSS, NS), p. 98.
ICML-2012-EbanBSG #learning #online #predict #sequence: Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICML-2011-Shalev-ShwartzGS #constraints #rank #scalability: Large-Scale Convex Minimization with a Low-Rank Constraint (SSS, AG, OS), pp. 329–336.
ICML-2011-UrnerSB #predict: Access to Unlabeled Data can Speed up Prediction Time (RU, SSS, SBD), pp. 641–648.
ICML-2010-Cesa-BianchiSS #learning #performance: Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
ICML-2009-Shalev-ShwartzT #probability: Stochastic methods for l1 regularized loss minimization (SSS, AT), pp. 929–936.
ICML-2008-DuchiSSC #learning #performance: Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICML-2008-KakadeST #algorithm #multi #online #performance #predict: Efficient bandit algorithms for online multiclass prediction (SMK, SSS, AT), pp. 440–447.
ICML-2008-Shalev-ShwartzS #dependence #optimisation #set: SVM optimization: inverse dependence on training set size (SSS, NS), pp. 928–935.
ICML-2007-Shalev-ShwartzSS #named: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM (SSS, YS, NS), pp. 807–814.
ICML-2006-FinkSSU #learning #multi #online: Online multiclass learning by interclass hypothesis sharing (MF, SSS, YS, SU), pp. 313–320.
ICML-2004-Shalev-ShwartzSN #learning #online #pseudo: Online and batch learning of pseudo-metrics (SSS, YS, AYN).
SIGIR-2002-Shalev-ShwartzDFS #modelling #query #robust: Robust temporal and spectral modeling for query By melody (SSS, SD, NF, YS), pp. 331–338.