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Open Knowledge
XHTML 1.0 W3C Rec
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Travelled to:
1 × Finland
1 × France
1 × Germany
1 × Israel
1 × United Kingdom
2 × China
3 × Canada
7 × USA
Collaborated with:
D.Janzing M.Gomez-Rodriguez K.Muandet A.J.Smola K.Zhang J.Peters D.Balduzzi C.Walder A.Gretton M.Wu N.D.Lawrence D.Lopez-Paz K.Fukumizu L.Song E.Sgouritsa J.M.Mooij P.Geiger M.Gong J.Leskovec V.Franc A.Zien P.Achlioptas K.M.Borgwardt P.O.Hoyer K.I.Kim F.Steinke V.Blanz S.Sonnenburg G.Rätsch O.Chapelle G.H.Bakir D.Zhou J.Huang C.Burges V.Vapnik I.Tolstikhin N.Shajarisales M.Besserve H.Daneshmand S.Kpotufe Z.Wang X.Sun K.Yu S.Yu J.Ham D.D.Lee S.Mika D.Tao B.K.Sriperumbudur S.Sra Z.Ghahramani X.Zhang D.W.Hogg D.Wang D.Foreman-Mackey C.Simon-Gabriel T.N.Lal M.Schröder N.J.Hill H.Preißl T.Hinterberger J.Mellinger M.Bogdan W.Rosenstiel T.Hofmann N.Birbaumer
Talks about:
causal (7) learn (7) kernel (5) model (5) infer (5) network (4) effect (4) diffus (4) estim (4) caus (4)

Person: Bernhard Schölkopf

DBLP DBLP: Sch=ouml=lkopf:Bernhard

Contributed to:

ICML 20152015
ICML c1 20142014
ICML c2 20142014
ICML c1 20132013
ICML c3 20132013
ICML 20122012
ICML 20112011
KDD 20112011
ICML 20102010
ICML 20092009
ICML 20082008
ICML 20072007
ICML 20052005
ICML 20042004
ICML 20012001
ICML 20002000
KDD 19951995

Wrote 35 papers:

ICML-2015-GeigerZSGJ #component #identification #process
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (PG, KZ, BS, MG, DJ), pp. 1917–1925.
Discovering Temporal Causal Relations from Subsampled Data (MG, KZ, BS, DT, PG), pp. 1898–1906.
ICML-2015-Lopez-PazMST #learning #towards
Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICML-2015-ScholkopfHWFJSP #fault
Removing systematic errors for exoplanet search via latent causes (BS, DWH, DW, DFM, DJ, CJSG, JP), pp. 2218–2226.
ICML-2015-ShajarisalesJSB #linear
Telling cause from effect in deterministic linear dynamical systems (NS, DJ, BS, MB), pp. 285–294.
ICML-c1-2014-MuandetFSGS #estimation #kernel
Kernel Mean Estimation and Stein Effect (KM, KF, BKS, AG, BS), pp. 10–18.
ICML-c2-2014-DaneshmandGSS #algorithm #complexity #network
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (HD, MGR, LS, BS), pp. 793–801.
ICML-c2-2014-KpotufeSJS #consistency
Consistency of Causal Inference under the Additive Noise Model (SK, ES, DJ, BS), pp. 478–486.
ICML-c2-2014-Lopez-PazSSGS #analysis #component #random
Randomized Nonlinear Component Analysis (DLP, SS, AJS, ZG, BS), pp. 1359–1367.
ICML-c1-2013-MuandetBS #invariant #representation
Domain Generalization via Invariant Feature Representation (KM, DB, BS), pp. 10–18.
ICML-c3-2013-Gomez-RodriguezLS #modelling
Modeling Information Propagation with Survival Theory (MGR, JL, BS), pp. 666–674.
ICML-c3-2013-ZhangSMW #adaptation
Domain Adaptation under Target and Conditional Shift (KZ, BS, KM, ZW), pp. 819–827.
ICML-2012-Gomez-RodriguezS #network
Influence Maximization in Continuous Time Diffusion Networks (MGR, BS), p. 78.
ICML-2012-Gomez-RodriguezS12a #multi #network
Submodular Inference of Diffusion Networks from Multiple Trees (MGR, BS), p. 206.
ICML-2012-ScholkopfJPSZM #learning #on the
On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
ICML-2011-FrancZS #modelling #probability
Support Vector Machines as Probabilistic Models (VF, AZ, BS), pp. 665–672.
ICML-2011-Gomez-RodriguezBS #network
Uncovering the Temporal Dynamics of Diffusion Networks (MGR, DB, BS), pp. 561–568.
Two-locus association mapping in subquadratic time (PA, BS, KMB), pp. 726–734.
Telling cause from effect based on high-dimensional observations (DJ, POH, BS), pp. 479–486.
ICML-2009-MooijJPS #dependence #modelling
Regression by dependence minimization and its application to causal inference in additive noise models (JMM, DJ, JP, BS), pp. 745–752.
ICML-2009-PetersJGS #detection
Detecting the direction of causal time series (JP, DJ, AG, BS), pp. 801–808.
ICML-2008-SongZSGS #estimation #kernel
Tailoring density estimation via reproducing kernel moment matching (LS, XZ, AJS, AG, BS), pp. 992–999.
ICML-2008-WalderKS #multi #process
Sparse multiscale gaussian process regression (CW, KIK, BS), pp. 1112–1119.
ICML-2007-SunJSF #algorithm #kernel #learning
A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICML-2007-WuYYS #learning
Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICML-2005-LalSHP #feedback #interface #online
A brain computer interface with online feedback based on magnetoencephalography (TNL, MS, NJH, HP, TH, JM, MB, WR, TH, NB, BS), pp. 465–472.
ICML-2005-ScholkopfSB #machine learning #problem
Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.
ICML-2005-SonnenburgRS #classification #scalability #sequence
Large scale genomic sequence SVM classifiers (SS, GR, BS), pp. 848–855.
ICML-2005-WalderCS #modelling #problem
Implicit surface modelling as an eigenvalue problem (CW, OC, BS), pp. 936–939.
ICML-2005-WuSB #classification #scalability
Building Sparse Large Margin Classifiers (MW, BS, GHB), pp. 996–1003.
ICML-2005-ZhouHS #graph #learning
Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.
ICML-2004-HamLMS #kernel #reduction
A kernel view of the dimensionality reduction of manifolds (JH, DDL, SM, BS).
ICML-2001-LawrenceS #kernel
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise (NDL, BS), pp. 306–313.
ICML-2000-SmolaS #approximate #machine learning #matrix
Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.
Extracting Support Data for a Given Task (BS, CB, VV), pp. 252–257.

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.