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Travelled to:
1 × Canada
1 × Finland
1 × Germany
2 × China
2 × United Kingdom
5 × USA
Collaborated with:
S.S.Keerthi P.Melville H.Avron S.P.Kasiviswanathan A.Ghoting H.Q.Minh D.S.Rosenberg A.Kumar P.Kambadur R.D.Lawrence O.Chapelle P.Niyogi M.Belkin J.Yang M.W.Mahoney S.Kale A.Banerjee T.Li C.H.Q.Ding Y.Zhang
Talks about:
supervis (4) learn (4) regular (3) semi (3) dictionari (2) transduct (2) manifold (2) kernel (2) spars (2) scale (2)

Person: Vikas Sindhwani

DBLP DBLP: Sindhwani:Vikas

Contributed to:

ICML c1 20142014
ICML c1 20132013
ICML 20122012
KDD 20122012
CIKM 20112011
ICML 20112011
ICML 20092009
SIGIR 20092009
ICML 20082008
ICML 20062006
SIGIR 20062006
ICML 20052005

Wrote 12 papers:

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-c1-2013-KumarSK #algorithm #matrix #performance
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization (AK, VS, PK), pp. 231–239.
ICML-2012-AvronKKS #performance #probability
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization (HA, SK, SPK, VS), p. 46.
KDD-2012-SindhwaniG #distributed #learning #scalability #taxonomy
Large-scale distributed non-negative sparse coding and sparse dictionary learning (VS, AG), pp. 489–497.
CIKM-2011-KasiviswanathanMBS #detection #learning #taxonomy #topic #using
Emerging topic detection using dictionary learning (SPK, PM, AB, VS), pp. 745–754.
ICML-2011-MinhS
Vector-valued Manifold Regularization (HQM, VS), pp. 57–64.
ICML-2009-SindhwaniML #design #nondeterminism
Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.
SIGIR-2009-LiSDZ #classification #sentiment
Knowledge transformation for cross-domain sentiment classification (TL, VS, CHQD, YZ), pp. 716–717.
ICML-2008-SindhwaniR #learning #multi
An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICML-2006-SindhwaniKC #kernel
Deterministic annealing for semi-supervised kernel machines (VS, SSK, OC), pp. 841–848.
SIGIR-2006-SindhwaniK #linear #scalability
Large scale semi-supervised linear SVMs (VS, SSK), pp. 477–484.
ICML-2005-SindhwaniNB #learning
Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
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.