-
3
-
-
4544371135
-
Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
-
K. Fukumizu, F. R. Bach, M. I. Jordan, and C. Williams. Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. Journal of Machine Learning Research, 5:73–99, 2004.
-
(2004)
Journal of Machine Learning Research
, vol.5
, pp. 73-99
-
-
Fukumizu, K.1
Bach, F.R.2
Jordan, M.I.3
Williams, C.4
-
4
-
-
85161986095
-
Kernel measures of conditional dependence
-
J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Cambridge, MA, MIT Press
-
K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf. Kernel measures of conditional dependence. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 489–496, Cambridge, MA, 2008. MIT Press.
-
(2008)
Advances in Neural Information Processing Systems 20, Pages 489–496
-
-
Fukumizu, K.1
Gretton, A.2
Sun, X.3
Schölkopf, B.4
-
5
-
-
84864063983
-
-
Cambridge, MA, MIT Press
-
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola. A kernel method for the two-sample-problem. In NIPS 19, pages 513–520, Cambridge, MA, 2007. MIT Press
-
(2007)
A Kernel Method for the Two-Sample-Problem. in NIPS 19, Pages
, pp. 513-520
-
-
Gretton, A.1
Borgwardt, K.2
Rasch, M.3
Schölkopf, B.4
Smola, A.5
-
6
-
-
70349847999
-
Covariate shift and local learning by distribution matching
-
editors, MIT Press, Cambridge, MA
-
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf. Covariate shift and local learning by distribution matching. In J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, editors, Dataset shift in machine learning, pages 131–160. MIT Press, Cambridge, MA, 2008.
-
(2008)
Dataset Shift in Machine Learning
, pp. 131-160
-
-
Gretton, A.1
Smola, A.2
Huang, J.3
Schmittfull, M.4
Borgwardt, K.5
Schölkopf, B.6
Quiñonero-Candela, J.7
Sugiyama, M.8
Schwaighofer, A.9
Lawrence, N.10
-
7
-
-
84877753617
-
Optimal kernel choice for large-scale two-sample tests
-
A. Gretton, B. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, and K. Fukumizu. Optimal kernel choice for large-scale two-sample tests. In NIPS 25. MIT Press, 2012.
-
(2012)
NIPS 25. MIT Press
-
-
Gretton, A.1
Sriperumbudur, B.2
Sejdinovic, D.3
Strathmann, H.4
Balakrishnan, S.5
Pontil, M.6
Fukumizu, K.7
-
8
-
-
14644421528
-
Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans
-
J. Ham, Y. Chen, M. M. Crawford, and J. Ghosh. Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens., 43(3):492–501, 2005.
-
(2005)
Geosci. Remote Sens.
, vol.43
, Issue.3
, pp. 492-501
-
-
Ham, J.1
Chen, Y.2
Crawford, M.M.3
Ghosh, J.4
-
9
-
-
84858789485
-
Nonlinear causal discovery with additive noise models
-
Vancouver, B.C., Canada
-
P.O. Hoyer, D. Janzing, J. Mooji, J. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 21, Vancouver, B.C., Canada, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, pp. 21
-
-
Hoyer, P.O.1
Janzing, D.2
Mooji, J.3
Peters, J.4
Schölkopf, B.5
-
10
-
-
84864031047
-
Correcting sample selection bias by unlabeled data
-
J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. In NIPS 19, pages 601– 608, 2007.
-
(2007)
NIPS 19, Pages 601– 608
-
-
Huang, J.1
Smola, A.2
Gretton, A.3
Borgwardt, K.4
Schölkopf, B.5
-
11
-
-
84857129458
-
Information-geometric approach to inferring causal directions
-
D. Janzing, J. Mooij, K. Zhang, J. Lemeire, J. Zscheischler, P. Daniuvsis, B. Steudel, and B. Schölkopf. Information-geometric approach to inferring causal directions. Artificial Intelligence, pages 1–31, 2012.
-
(2012)
Artificial Intelligence
, pp. 1-31
-
-
Janzing, D.1
Mooij, J.2
Zhang, K.3
Lemeire, J.4
Zscheischler, J.5
Daniuvsis, P.6
Steudel, B.7
Schölkopf, B.8
-
12
-
-
33845536164
-
The class imbalance problem: A systematic study
-
N. Japkowicz and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6:429–450, 2002.
-
(2002)
Intelligent Data Analysis
, vol.6
, pp. 429-450
-
-
Japkowicz, N.1
Stephen, S.2
-
14
-
-
0036161029
-
Support vector machines for classification in nonstandard situations
-
Y. Lin, Y. Lee, and G. Wahba. Support vector machines for classification in nonstandard situations. Machine Learning, 46:191–202, 2002.
-
(2002)
Machine Learning
, vol.46
, pp. 191-202
-
-
Lin, Y.1
Lee, Y.2
Wahba, G.3
-
15
-
-
0000847211
-
The estimation of choice probabilities from choice-based samples
-
C. Manski and S. Lerman. The estimation of choice probabilities from choice-based samples. Econometrica, 45:1977–1988, 1977.
-
(1977)
Econometrica
, vol.45
, pp. 1977-1988
-
-
Manski, C.1
Lerman, S.2
-
16
-
-
85162054206
-
Probabilistic latent variable models for distinguishing between cause and effect
-
Curran, NY, USA
-
J. Mooij, O. Stegle, D. Janzing, K. Zhang, and B. Schölkopf. Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems 23 (NIPS 2010), Curran, NY, USA, 2010.
-
(2010)
Advances in Neural Information Processing Systems 23 (NIPS
, pp. 2010
-
-
Mooij, J.1
Stegle, O.2
Janzing, D.3
Zhang, K.4
Schölkopf, B.5
-
19
-
-
84870901798
-
Interactive domain adaptation for the classification of remote sensing images using active learning
-
C. Persello. Interactive domain adaptation for the classification of remote sensing images using active learning. IEEE Geoscience and Remote Sensing Letters, 10:736–740, 2013.
-
(2013)
IEEE Geoscience and Remote Sensing Letters
, vol.10
, pp. 736-740
-
-
Persello, C.1
-
20
-
-
85141075949
-
Monte Carlo Statistical Methods. Springer Press, New York
-
C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer Press, New York, 2nd edition, 2004.
-
(2004)
2Nd Edition
-
-
Robert, C.P.1
Casella, G.2
-
21
-
-
0002619965
-
Ridge regression learning algorithm in dual variables
-
Madison, WI
-
C. Saunders, A. Gammerman, and V. Vovk. Ridge regression learning algorithm in dual variables. In 15th International Conference on Machine Learning, pages 515–521, Madison, WI, 1998.
-
(1998)
15Th International Conference on Machine Learning
, pp. 515-521
-
-
Saunders, C.1
Gammerman, A.2
Vovk, V.3
-
22
-
-
84867113617
-
On causal and anticausal learning
-
Edinburgh, Scotland
-
B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij. On causal and anticausal learning. In Proc. 29th International Conference on Machine Learning (ICML 2012), Edinburgh, Scotland, 2012.
-
(2012)
Proc. 29Th International Conference on Machine Learning (ICML
, pp. 2012
-
-
Schölkopf, B.1
Janzing, D.2
Peters, J.3
Sgouritsa, E.4
Zhang, K.5
Mooij, J.6
-
23
-
-
0347243182
-
Nonlinear component analysis as a kernel eigenvalue problem
-
B. Schölkopf, A. Smola, and K. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299–1319, 1998.
-
(1998)
Neural Computation
, vol.10
, pp. 1299-1319
-
-
Schölkopf, B.1
Smola, A.2
Muller, K.3
-
24
-
-
33749326177
-
A linear non- Gaussian acyclic model for causal discovery
-
S. Shimizu, P.O. Hoyer, A. Hyvärinen, and A.J. Kerminen. A linear non- Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003–2030, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2003-2030
-
-
Shimizu, S.1
Hoyer, P.O.2
Hyvärinen, A.3
Kerminen, A.J.4
-
25
-
-
0037527188
-
Improving predictive inference under covariate shift by weighting the log-likelihood function
-
H. Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90:227–244, 2000.
-
(2000)
Journal of Statistical Planning and Inference
, vol.90
, pp. 227-244
-
-
Shimodaira, H.1
-
26
-
-
38149136576
-
A Hilbert space embedding for distributions
-
A. Smola, A. Gretton, L. Song, and B. Schölkopf. A Hilbert space embedding for distributions. In Proceedings of the 18th International Conference on Algorithmic Learning Theory, pages 13–31. Springer-Verlag, 2007.
-
(2007)
Proceedings of the 18Th International Conference on Algorithmic Learning Theory, Pages 13–31. Springer-Verlag
-
-
Smola, A.1
Gretton, A.2
Song, L.3
Schölkopf, B.4
-
27
-
-
77956540831
-
Hilbert space embeddings of hidden Markov models
-
Haifa, Israel
-
L. Song, B. Boots, S. Siddiqi, G. Gordon, and A. Smola. Hilbert space embeddings of hidden Markov models. In Proceedings of the 26th International Conference on Machine Learning, Haifa, Israel, 2010.
-
Proceedings of the 26Th International Conference on Machine Learning
, pp. 2010
-
-
Song, L.1
Boots, B.2
Siddiqi, S.3
Gordon, G.4
Smola, A.5
-
29
-
-
85142005424
-
Causation, Prediction, and Search. MIT Press, Cambridge, MA
-
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, Cambridge, MA, 2nd edition, 2001.
-
(2001)
2Nd Edition
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
30
-
-
80052235767
-
Universality, characteristic kernels and RKHS embedding of measures
-
B. Sriperumbudur, K. Fukumizu, and G. Lanckriet. Universality, characteristic kernels and RKHS embedding of measures. Journal of Machine Learning Research, 12:2389–2410, 2011.
-
(2011)
Journal of Machine Learning Research
, vol.12
, pp. 2389-2410
-
-
Sriperumbudur, B.1
Fukumizu, K.2
Lanckriet, G.3
-
31
-
-
80052700798
-
When training and test sets are different: Characterizing learning transfer
-
A. Storkey. When training and test sets are different: Characterizing learning transfer. In J. Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, editors, Dataset Shift in Machine Learning, pages 3–28. MIT Press, 2009.
-
(2009)
J. Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, Editors, Dataset Shift in Machine Learning, Pages 3–28. MIT Press
-
-
Storkey1
-
33
-
-
55549114317
-
Direct importance estimation for covariate shift adaptation
-
M. Sugiyama, T. Suzuki, S. Nakajima, H. Kashima, P. von Bünau, and M. Kawanabe. Direct importance estimation for covariate shift adaptation. Annals of the Institute of Statistical Mathematics, 60:699–746, 2008.
-
(2008)
Annals of the Institute of Statistical Mathematics
, vol.60
, pp. 699-746
-
-
Sugiyama, M.1
Suzuki, T.2
Nakajima, S.3
Kashima, H.4
von Bünau, P.5
Kawanabe, M.6
-
35
-
-
85142074219
-
Making things happen: A theory of causal explanation. Oxford University Press
-
J. Woodward. Making things happen: A theory of causal explanation. Oxford University Press, New York, 2003.
-
(2003)
New York
-
-
Woodward, J.1
-
36
-
-
84897557514
-
A framework for modeling positive class expansion with single snapshot
-
Y. Yu and Z. H. Zhou. A framework for modeling positive class expansion with single snapshot. In PAKDD 2008, 2008.
-
(2008)
PAKDD 2008
-
-
Yu, Y.1
Zhou, Z.H.2
-
40
-
-
80053139998
-
Kernel-based conditional independence test and application in causal discovery
-
Barcelona, Spain
-
K. Zhang, J. Peters, D. Janzing, and B. Schölkopf. Kernel-based conditional independence test and application in causal discovery. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, 2011.
-
(2011)
Proceedings of the 27Th Conference on Uncertainty in Artificial Intelligence (UAI
, pp. 2011
-
-
Zhang, K.1
Peters, J.2
Janzing, D.3
Schölkopf, B.4
-
41
-
-
84897514005
-
Domain adaptation under target and conditional shift
-
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang. Domain adaptation under target and conditional shift. In Proceedings of the 30th International Conference on Machine Learning, JMLR: W&CP Vol. 28, 2013
-
(2013)
Proceedings of the 30Th International Conference on Machine Learning, JMLR: W&CP Vol. 28
-
-
Zhang, K.1
Schölkopf, B.2
Muandet, K.3
Wang, Z.4
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