BibSLEIGH — Alexander_J

Travelled to:

1 × Austria
1 × Finland
1 × France
1 × Israel
1 × Switzerland
1 × United Kingdom
13 × USA
2 × Australia
2 × Canada
2 × Germany
3 × China

Collaborated with:

A.Ahmed Q.V.Le Y.Wang L.Song S.V.N.Vishwanathan B.Schölkopf C.S.Ong A.Gretton M.Li T.Gärtner S.Canu Y.Low V.Josifovski S.M.Narayanamurthy T.S.Caetano V.Nikulin K.M.Borgwardt M.Aly S.Yang B.Long H.Zha D.G.Andersen S.E.Fienberg S.R.Flaxman Z.Liu R.J.Tibshirani L.Hong T.Sarlós N.d.Freitas M.Zoghi S.Matsushima D.Agarwal M.Weimer A.Karatzoglou Y.Altun T.Hofmann M.N.Murty C.Campbell N.Cristianini L.Zhou A.Q.Li S.Ravi T.Zhang Y.Chen J.Huang K.Fukumizu N.Quadrianto C.H.Teo J.J.McAuley M.O.Franz X.Mary P.A.Flach A.Kowalczyk S.Flaxman A.G.Wilson D.Neill H.Nickisch N.Du M.Farajtabar W.Zhang J.Yang D.Lopez-Paz S.Sra Z.Ghahramani C.Tan E.H.Chi D.A.Huffaker G.Kossinets A.Das T.Anastasakos Y.Chang Z.Zheng B.Boots S.M.Siddiqi G.J.Gordon K.Q.Weinberger A.Dasgupta J.Langford J.Attenberg X.Zhang J.Bedo Q.Diao M.Qiu C.Wu J.Jiang C.Wang J.W.Park J.Long E.J.Shekita B.Su

Talks about:

learn (8) process (6) model (6) gaussian (5) machin (5) distribut (4) applic (4) space (4) user (4) support (3)

Person: Alexander J. Smola

DBLP: Smola:Alexander_J=

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2000

Wrote 45 papers:

ICML-2015-FlaxmanWNNS #performance #process: Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods (SF, AGW, DN, HN, AJS), pp. 607–616.
ICML-2015-WangFS #for free #monte carlo #privacy #probability: Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (YXW, SEF, AJS), pp. 2493–2502.
KDD-2015-DuFASS #clustering #documentation #process: Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams (ND, MF, AA, AJS, LS), pp. 219–228.
KDD-2015-FlaxmanWS: Who Supported Obama in 2012?: Ecological Inference through Distribution Regression (SRF, YXW, AJS), pp. 289–298.
KDD-2015-ZhangAYJS #email #information management: Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails (WZ, AA, JY, VJ, AJS), pp. 2257–2266.
KDD-2015-ZhouALS #algebra #linear: Cuckoo Linear Algebra (LZ, DGA, ML, AJS), pp. 1553–1562.
RecSys-2015-LiuWS #matrix #performance: Fast Differentially Private Matrix Factorization (ZL, YXW, AJS), pp. 171–178.
ICML-c2-2014-Lopez-PazSSGS #analysis #component #random: Randomized Nonlinear Component Analysis (DLP, SS, AJS, ZG, BS), pp. 1359–1367.
ICML-c2-2014-WangST #statistics: The Falling Factorial Basis and Its Statistical Applications (YXW, AJS, RJT), pp. 730–738.
KDD-2014-DiaoQWSJW #aspect-oriented #modelling #recommendation #sentiment: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (QD, MQ, CYW, AJS, JJ, CW), pp. 193–202.
KDD-2014-LiARS #complexity #modelling #topic: Reducing the sampling complexity of topic models (AQL, AA, SR, AJS), pp. 891–900.
KDD-2014-LiZCS #optimisation #performance #probability: Efficient mini-batch training for stochastic optimization (ML, TZ, YC, AJS), pp. 661–670.
OSDI-2014-LiAPSAJLSS #distributed #machine learning #parametricity #scalability: Scaling Distributed Machine Learning with the Parameter Server (ML, DGA, JWP, AJS, AA, VJ, JL, EJS, BYS), pp. 583–598.
CIKM-2013-TanCHKS #predict: Instant foodie: predicting expert ratings from grassroots (CT, EHC, DAH, GK, AJS), pp. 1127–1136.
ICML-c3-2013-AhmedHS #documentation #modelling #process: Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling (AA, LH, AJS), pp. 1426–1434.
ICML-c3-2013-LeSS #named: Fastfood — Computing Hilbert Space Expansions in loglinear time (QVL, TS, AJS), pp. 244–252.
KDD-2013-AhmedS #modelling #parametricity #scalability: The dataminer’s guide to scalable mixed-membership and nonparametric bayesian models (AA, AJS), p. 1529.
CIKM-2012-AhmedADSA #behaviour #feature model #multi: Web-scale multi-task feature selection for behavioral targeting (AA, MA, AD, AJS, TA), pp. 1737–1741.
ICML-2012-FreitasSZ #bound #exponential #process: Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (NdF, AJS, MZ), p. 125.
KDD-2012-MatsushimaVS #linear: Linear support vector machines via dual cached loops (SM, SVNV, AJS), pp. 177–185.
SIGIR-2012-YangSLZC #network #predict #social: Friend or frenemy?: predicting signed ties in social networks (SHY, AJS, BL, HZ, YC), pp. 555–564.
KDD-2011-AhmedLAJS #behaviour #distributed #scalability: Scalable distributed inference of dynamic user interests for behavioral targeting (AA, YL, MA, VJ, AJS), pp. 114–122.
KDD-2011-LowAS #multi #personalisation: Multiple domain user personalization (YL, DA, AJS), pp. 123–131.
SIGIR-2011-YangLSZZ #collaboration #learning #recommendation #using: Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
ICML-2010-SongSGS #markov #modelling: Hilbert Space Embeddings of Hidden Markov Models (LS, BB, SMS, GJG, AJS), pp. 991–998.
VLDB-2010-SmolaN #architecture #modelling #parallel #topic: An Architecture for Parallel Topic Models (AJS, SMN), pp. 703–710.
ICML-2009-SongHSF: Hilbert space embeddings of conditional distributions with applications to dynamical systems (LS, JH, AJS, KF), pp. 961–968.
ICML-2009-WeinbergerDLSA #learning #multi #scalability: Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.
ICML-2008-QuadriantoSCL: Estimating labels from label proportions (NQ, AJS, TSC, QVL), pp. 776–783.
ICML-2008-SongZSGS #estimation #kernel: Tailoring density estimation via reproducing kernel moment matching (LS, XZ, AJS, AG, BS), pp. 992–999.
RecSys-2008-WeimerKS #adaptation #collaboration: Adaptive collaborative filtering (MW, AK, AJS), pp. 275–282.
ICML-2007-SongSGB #clustering #dependence: A dependence maximization view of clustering (LS, AJS, AG, KMB), pp. 815–822.
ICML-2007-SongSGBB #dependence #estimation #feature model: Supervised feature selection via dependence estimation (LS, AJS, AG, KMB, JB), pp. 823–830.
KDD-2007-TeoSVL #composition #scalability: A scalable modular convex solver for regularized risk minimization (CHT, AJS, SVNV, QVL), pp. 727–736.
ICML-2006-LeSG #knowledge-based: Simpler knowledge-based support vector machines (QVL, AJS, TG), pp. 521–528.
ICML-2006-McAuleyCSF #higher-order #image #learning: Learning high-order MRF priors of color images (JJM, TSC, AJS, MOF), pp. 617–624.
ICML-2005-LeSC #process: Heteroscedastic Gaussian process regression (QVL, AJS, SC), pp. 489–496.
MLDM-2005-NikulinS #clustering #probability: Universal Clustering with Regularization in Probabilistic Space (VN, AJS), pp. 142–152.
ICML-2004-AltunHS #classification #process #sequence: Gaussian process classification for segmenting and annotating sequences (YA, TH, AJS).
ICML-2004-OngMCS #kernel #learning: Learning with non-positive kernels (CSO, XM, SC, AJS).
ICML-2003-OngS #kernel #machine learning: Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.
ICML-2003-VishwanathanSM: SimpleSVM (SVNV, AJS, MNM), pp. 760–767.
ICML-2002-GartnerFKS #kernel #multi: Multi-Instance Kernels (TG, PAF, AK, AJS), pp. 179–186.
ICML-2000-CampbellCS #classification #learning #query #scalability: Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.
ICML-2000-SmolaS #approximate #machine learning #matrix: Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.