Masashi Sugiyama

2008
42		Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters: Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. AAAI 2008: 1351-1356
41	EE	Masashi Sugiyama, Shinichi Nakajima: Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) 2008: 406-422
40	EE	Akiko Takeda, Masashi Sugiyama: nu-support vector machine as conditional value-at-risk minimization. ICML 2008: 1056-1063
39	EE	Neil Rubens, Vera Sheinman, Takenobu Tokunaga, Masashi Sugiyama: Order Retrieval. LKR 2008: 310-317
38	EE	Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese: Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD 2008: 333-344
37	EE	Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama: Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. SDM 2008: 443-454
36	EE	Masashi Sugiyama, Neil Rubens: Active Learning with Model Selection in Linear Regression. SDM 2008: 518-529
35	EE	Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama: Integration of Multiple Networks for Robust Label Propagation. SDM 2008: 716-726
34	EE	Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Klaus-Robert Müller: Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise. IEICE Transactions 91-D(5): 1577-1580 (2008)
2007
33	EE	Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller: Asymptotic Bayesian generalization error when training and test distributions are different. ICML 2007: 1079-1086
32	EE	Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar: Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. ICRA 2007: 1733-1740
31	EE	Neil Rubens, Masashi Sugiyama: Influence-based collaborative active learning. RecSys 2007: 145-148
30	EE	Shun Gokita, Masashi Sugiyama, Keisuke Sakurai: Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion. IEICE Transactions 90-A(11): 2584-2592 (2007)
29	EE	Masashi Sugiyama: Generalization Error Estimation for Non-linear Learning Methods. IEICE Transactions 90-A(7): 1496-1499 (2007)
28	EE	Yasushi Hidaka, Masashi Sugiyama: A New Meta-Criterion for Regularized Subspace Information Criterion. IEICE Transactions 90-D(11): 1779-1786 (2007)
2006
27	EE	Masashi Sugiyama, Benjamin Blankertz, Matthias Krauledat, Guido Dornhege, Klaus-Robert Müller: Importance-Weighted Cross-Validation for Covariate Shift. DAGM-Symposium 2006: 354-363
26	EE	Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller: A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. ICA 2006: 149-156
25	EE	Masashi Sugiyama: Local Fisher discriminant analysis for supervised dimensionality reduction. ICML 2006: 905-912
24	EE	Amos J. Storkey, Masashi Sugiyama: Mixture Regression for Covariate Shift. NIPS 2006: 1337-1344
23	EE	Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi: Model Selection Using a Class of Kernels with an Invariant Metric. SSPR/SPR 2006: 862-870
22	EE	Masashi Sugiyama, Keisuke Sakurai: Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion. IEICE Transactions 89-A(8): 2216-2225 (2006)
21	EE	Masashi Sugiyama, Hidemitsu Ogawa: Constructing Kernel Functions for Binary Regression. IEICE Transactions 89-D(7): 2243-2249 (2006)
20	EE	Masashi Sugiyama: Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. Journal of Machine Learning Research 7: 141-166 (2006)
19	EE	Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller: In Search of Non-Gaussian Components of a High-Dimensional Distribution. Journal of Machine Learning Research 7: 247-282 (2006)
2005
18	EE	Masashi Sugiyama, Klaus-Robert Müller: Model Selection Under Covariate Shift. ICANN (2) 2005: 235-240
17	EE	Masashi Sugiyama: Active Learning for Misspecified Models. NIPS 2005
16	EE	Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir Spokoiny, Klaus-Robert Müller: Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. NIPS 2005
2004
15	EE	Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller: Regularizing generalization error estimators: a novel approach to robust model selection. ESANN 2004: 163-168
14		Masashi Sugiyama: Estimating the error at given test input points for linear regression. Neural Networks and Computational Intelligence 2004: 113-118
13	EE	Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller: Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression. Neural Computation 16(5): 1077-1104 (2004)
2002
12	EE	Masashi Sugiyama, Klaus-Robert Müller: Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. ICANN 2002: 528-534
11	EE	Masashi Sugiyama, Klaus-Robert Müller: The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. Journal of Machine Learning Research 3: 323-359 (2002)
10		Masashi Sugiyama, Hidemitsu Ogawa: Theoretical and Experimental Evaluation of the Subspace Information Criterion. Machine Learning 48(1-3): 25-50 (2002)
9	EE	Masashi Sugiyama, Hidemitsu Ogawa: Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Networks 15(3): 349-361 (2002)
8	EE	Masashi Sugiyama, Hidemitsu Ogawa: A unified method for optimizing linear image restoration filters. Signal Processing 82(11): 1773-1787 (2002)
2001
7		Masashi Sugiyama, Hidemitsu Ogawa: Incremental Active Learning for Optimal Generalization. Neural Computation 12(12): 2909-2940 (2001)
6		Masashi Sugiyama, Hidemitsu Ogawa: Subspace Information Criterion for Model Selection. Neural Computation 13(8): 1863-1889 (2001)
5	EE	Masashi Sugiyama, Hidemitsu Ogawa: Incremental projection learning for optimal generalization. Neural Networks 14(1): 53-66 (2001)
4	EE	Masashi Sugiyama, Hidemitsu Ogawa: Properties of incremental projection learning. Neural Networks 14(1): 67-78 (2001)
2000
3	EE	Masashi Sugiyama, Hidemitsu Ogawa: A new information criterion for the selection of subspace models. ESANN 2000: 69-74
2	EE	Masashi Sugiyama, Hidemitsu Ogawa: Incremental Active Learning with Bias Reduction. IJCNN (1) 2000: 15-20
1999
1	EE	Masashi Sugiyama, Hidemitsu Ogawa: Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. NIPS 1999: 624-630

Coauthor Index

1	Takayuki Akiyama	[42]
2	Steffen Bickel	[37]
3	Gilles Blanchard	[16] [19] [26] [34]
4	Benjamin Blankertz	[27]
5	Guido Dornhege	[27]
6	Shun Gokita	[30]
7	Hirotaka Hachiya	[32] [42]
8	Yasushi Hidaka	[28]
9	Shohei Hido	[37]
10	Tsuyoshi Idé	[38]
11	Hideyuki Imai	[23]
12	Hisashi Kashima	[35] [37]
13	Tsuyoshi Kato	[35]
14	Motoaki Kawanabe	[13] [15] [16] [19] [26] [33] [34]
15	Matthias Krauledat	[27]
16	Mineichi Kudo	[23]
17	Masaaki Miyakoshi	[23]
18	Klaus-Robert Müller	[11] [12] [13] [15] [16] [18] [19] [26] [27] [33] [34]
19	Shinichi Nakajima	[38] [41]
20	Hidemitsu Ogawa	[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [21]
21	Jan Peters	[42]
22	Neil Rubens	[31] [36] [39]
23	Keisuke Sakurai	[22] [30]
24	Jun Sese	[38]
25	Vera Sheinman	[39]
26	Vladimir Spokoiny	[16] [19] [26]
27	Amos J. Storkey	[24]
28	Akiko Takeda	[40]
29	Akira Tanaka	[23]
30	Takenobu Tokunaga	[39]
31	Christopher Towell	[32]
32	Yuta Tsuboi	[37]
33	Sethu Vijayakumar	[32]
34	Sumio Watanabe	[33]
35	Keisuke Yamazaki	[33]

Colors in the list of coauthors