-
1
-
-
84867122021
-
-
http://pl.is.tue.mpg.de/p/causal-anticausal.
-
-
-
-
2
-
-
33749246419
-
Efficient co-regularised least squares regression
-
Brefeld, U., Gärtner, T., Scheffer, T., and Wrobel, S. Efficient co-regularised least squares regression. In ICML, 2006.
-
(2006)
ICML
-
-
Brefeld, U.1
Gärtner, T.2
Scheffer, T.3
Wrobel, S.4
-
3
-
-
33749252873
-
-
MIT Press, Cambridge, MA, USA
-
Chapelle, O., Schölkopf, B., and Zien, A. Semi-Supervised Learning. MIT Press, Cambridge, MA, USA, 2006.
-
(2006)
Semi-Supervised Learning
-
-
Chapelle, O.1
Schölkopf, B.2
Zien, A.3
-
4
-
-
80053150280
-
Inferring deterministic causal relations
-
Daniušis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., and Schölkopf, B. Inferring deterministic causal relations. In UAI, 2010.
-
(2010)
UAI
-
-
Daniušis, P.1
Janzing, D.2
Mooij, J.3
Zscheischler, J.4
Steudel, B.5
Zhang, K.6
Schölkopf, B.7
-
5
-
-
79951750541
-
An extensive empirical study on semi-supervised learning
-
Guo, Y., Niu, X., and Zhang, H. An extensive empirical study on semi-supervised learning. In ICDM, 2010.
-
(2010)
ICDM
-
-
Guo, Y.1
Niu, X.2
Zhang, H.3
-
6
-
-
84858789485
-
Nonlinear causal discovery with additive noise models
-
Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., and Schölkopf, B. Nonlinear causal discovery with additive noise models. In NIPS, 2009.
-
(2009)
NIPS
-
-
Hoyer, P.O.1
Janzing, D.2
Mooij, J.M.3
Peters, J.4
Schölkopf, B.5
-
7
-
-
77956667205
-
Causal inference using the algorithmic Markov condition
-
Janzing, D. and Schölkopf, B. Causal inference using the algorithmic Markov condition. IEEE Transactions on Information Theory, 56(10):5168-5194, 2010.
-
(2010)
IEEE Transactions on Information Theory
, vol.56
, Issue.10
, pp. 5168-5194
-
-
Janzing, D.1
Schölkopf, B.2
-
9
-
-
13844277731
-
Storks deliver babies (p = 0.008)
-
Matthews, R. Storks deliver babies (p = 0.008). Teaching Statistics, 22(2):36-38, 2000.
-
(2000)
Teaching Statistics
, vol.22
, Issue.2
, pp. 36-38
-
-
Matthews, R.1
-
10
-
-
71149096052
-
Regression by dependence minimization and its application to causal inference in additive noise models
-
Mooij, J., Janzing, D., Peters, J., and Schölkopf, B. Regression by dependence minimization and its application to causal inference in additive noise models. In ICML, 2009.
-
(2009)
ICML
-
-
Mooij, J.1
Janzing, D.2
Peters, J.3
Schölkopf, B.4
-
12
-
-
0004213845
-
-
Cambridge University Press
-
Pearl, J. Causality. Cambridge University Press, 2000.
-
(2000)
Causality
-
-
Pearl, J.1
-
14
-
-
84867117422
-
-
arXiv:1112.2738v1 [stat.ML]
-
Schölkopf, B., Janzing, D., Peters, J., and Zhang, K. Robust learning via cause-effect models. arXiv:1112.2738v1 [stat.ML], 2011.
-
(2011)
Robust Learning Via Cause-effect Models
-
-
Schölkopf, B.1
Janzing, D.2
Peters, J.3
Zhang, K.4
-
15
-
-
70049090801
-
An empirical analysis of domain adaptation algorithms for genomic sequence analysis
-
Schweikert, G., Widmer, C., Schölkopf, B., and Rätsch, G. An empirical analysis of domain adaptation algorithms for genomic sequence analysis. In NIPS, 2009.
-
(2009)
NIPS
-
-
Schweikert, G.1
Widmer, C.2
Schölkopf, B.3
Rätsch, G.4
-
16
-
-
0003614273
-
-
Springer-Verlag. 2nd edition MIT Press
-
Spirtes, P., Glymour, C., and Scheines, R. Causation, prediction, and search. Springer-Verlag. (2nd edition MIT Press 2000), 1993.
-
(1993)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
17
-
-
80052700798
-
When training and test sets are different: Characterizing learning transfer
-
MIT Press
-
Storkey, A. When training and test sets are different: characterizing learning transfer. In Dataset Shift in Machine Learning. MIT Press, 2009.
-
(2009)
Dataset Shift in Machine Learning
-
-
Storkey, A.1
-
19
-
-
80053155838
-
On the identifiability of the post-nonlinear causal model
-
Zhang, K. and Hyvärinen, A. On the identifiability of the post-nonlinear causal model. In UAI, 2009.
-
(2009)
UAI
-
-
Zhang, K.1
Hyvärinen, A.2
|