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: Sch=ouml=lkopf:Bernhard
Contributed to:
2015201420142013201320122011
2011201020092008200720052004200120001995Wrote 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.
- ICML-2015-GongZSTG
- 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.
- KDD-2011-AchlioptasSB
- Two-locus association mapping in subquadratic time (PA, BS, KMB), pp. 726–734.
- ICML-2010-JanzingHS
- 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.
- KDD-1995-ScholkopfBV
- Extracting Support Data for a Given Task (BS, CB, VV), pp. 252–257.