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## Search the dblp DataBase
Masashi Sugiyama:
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## Publications of Author- Masashi Sugiyama, Benjamin Blankertz, Matthias Krauledat, Guido Dornhege, Klaus-Robert Müller
**Importance-Weighted Cross-Validation for Covariate Shift.**[Citation Graph (0, 0)][DBLP] DAGM-Symposium, 2006, pp:354-363 [Conf] - Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller
**Regularizing generalization error estimators: a novel approach to robust model selection.**[Citation Graph (0, 0)][DBLP] ESANN, 2004, pp:163-168 [Conf] - Masashi Sugiyama, Hidemitsu Ogawa
**A new information criterion for the selection of subspace models.**[Citation Graph (0, 0)][DBLP] ESANN, 2000, pp:69-74 [Conf] - Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller
**A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces.**[Citation Graph (0, 0)][DBLP] ICA, 2006, pp:149-156 [Conf] - Masashi Sugiyama, Klaus-Robert Müller
**Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces.**[Citation Graph (0, 0)][DBLP] ICANN, 2002, pp:528-534 [Conf] - Masashi Sugiyama, Klaus-Robert Müller
**Model Selection Under Covariate Shift.**[Citation Graph (0, 0)][DBLP] ICANN (2), 2005, pp:235-240 [Conf] - Masashi Sugiyama
**Local Fisher discriminant analysis for supervised dimensionality reduction.**[Citation Graph (0, 0)][DBLP] ICML, 2006, pp:905-912 [Conf] - Masashi Sugiyama, Hidemitsu Ogawa
**Incremental Active Learning with Bias Reduction.**[Citation Graph (0, 0)][DBLP] IJCNN (1), 2000, pp:15-20 [Conf] - Masashi Sugiyama
**Estimating the error at given test input points for linear regression.**[Citation Graph (0, 0)][DBLP] Neural Networks and Computational Intelligence, 2004, pp:113-118 [Conf] - Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir Spokoiny, Klaus-Robert Müller
**Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction.**[Citation Graph (0, 0)][DBLP] NIPS, 2005, pp:- [Conf] - Masashi Sugiyama
**Active Learning for Misspecified Models.**[Citation Graph (0, 0)][DBLP] NIPS, 2005, pp:- [Conf] - Masashi Sugiyama, Hidemitsu Ogawa
**Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.**[Citation Graph (0, 0)][DBLP] NIPS, 1999, pp:624-630 [Conf] - Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi
**Model Selection Using a Class of Kernels with an Invariant Metric.**[Citation Graph (0, 0)][DBLP] SSPR/SPR, 2006, pp:862-870 [Conf] - Masashi Sugiyama, Klaus-Robert Müller
**The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces.**[Citation Graph (0, 0)][DBLP] Journal of Machine Learning Research, 2002, v:3, n:, pp:323-359 [Journal] - Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir Spokoiny, Klaus-Robert Müller
**In Search of Non-Gaussian Components of a High-Dimensional Distribution.**[Citation Graph (0, 0)][DBLP] Journal of Machine Learning Research, 2006, v:7, n:, pp:247-282 [Journal] - Masashi Sugiyama
**Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error.**[Citation Graph (0, 0)][DBLP] Journal of Machine Learning Research, 2006, v:7, n:, pp:141-166 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Theoretical and Experimental Evaluation of the Subspace Information Criterion.**[Citation Graph (0, 0)][DBLP] Machine Learning, 2002, v:48, n:1-3, pp:25-50 [Journal] - Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller
**Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression.**[Citation Graph (0, 0)][DBLP] Neural Computation, 2004, v:16, n:5, pp:1077-1104 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Subspace Information Criterion for Model Selection.**[Citation Graph (0, 0)][DBLP] Neural Computation, 2001, v:13, n:8, pp:1863-1889 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Incremental Active Learning for Optimal Generalization.**[Citation Graph (0, 0)][DBLP] Neural Computation, 2001, v:12, n:12, pp:2909-2940 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Incremental projection learning for optimal generalization.**[Citation Graph (0, 0)][DBLP] Neural Networks, 2001, v:14, n:1, pp:53-66 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Properties of incremental projection learning.**[Citation Graph (0, 0)][DBLP] Neural Networks, 2001, v:14, n:1, pp:67-78 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**Optimal design of regularization term and regularization parameter by subspace information criterion.**[Citation Graph (0, 0)][DBLP] Neural Networks, 2002, v:15, n:3, pp:349-361 [Journal] - Masashi Sugiyama, Hidemitsu Ogawa
**A unified method for optimizing linear image restoration filters.**[Citation Graph (0, 0)][DBLP] Signal Processing, 2002, v:82, n:11, pp:1773-1787 [Journal] - Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller
**Asymptotic Bayesian generalization error when training and test distributions are different.**[Citation Graph (0, 0)][DBLP] ICML, 2007, pp:1079-1086 [Conf] - Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar
**Value Function Approximation on Non-Linear Manifolds for Robot Motor Control.**[Citation Graph (0, 0)][DBLP] ICRA, 2007, pp:1733-1740 [Conf] - Amos J. Storkey, Masashi Sugiyama
**Mixture Regression for Covariate Shift.**[Citation Graph (0, 0)][DBLP] NIPS, 2006, pp:1337-1344 [Conf] **Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation.**[Citation Graph (, )][DBLP]**Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise.**[Citation Graph (, )][DBLP]**On the Margin Explanation of Boosting Algorithms.**[Citation Graph (, )][DBLP]**Inlier-Based Outlier Detection via Direct Density Ratio Estimation.**[Citation Graph (, )][DBLP][Citation Graph (, )][DBLP]*nu*-support vector machine as conditional value-at-risk minimization.**Implicit Regularization in Variational Bayesian Matrix Factorization.**[Citation Graph (, )][DBLP]**Nonparametric Return Distribution Approximation for Reinforcement Learning.**[Citation Graph (, )][DBLP]**A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices.**[Citation Graph (, )][DBLP]**Least absolute policy iteration for robust value function approximation.**[Citation Graph (, )][DBLP]**Estimating Squared-Loss Mutual Information for Independent Component Analysis.**[Citation Graph (, )][DBLP]**Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning.**[Citation Graph (, )][DBLP]**Multi-Task Learning via Conic Programming.**[Citation Graph (, )][DBLP]**Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.**[Citation Graph (, )][DBLP]**Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection.**[Citation Graph (, )][DBLP]**Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction.**[Citation Graph (, )][DBLP]**Analysis of Variational Bayesian Matrix Factorization.**[Citation Graph (, )][DBLP]**Pool-Based Agnostic Experiment Design in Linear Regression.**[Citation Graph (, )][DBLP]**Efficient Sample Reuse in EM-Based Policy Search.**[Citation Graph (, )][DBLP]**Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information.**[Citation Graph (, )][DBLP]**Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation.**[Citation Graph (, )][DBLP]**Integration of Multiple Networks for Robust Label Propagation.**[Citation Graph (, )][DBLP]**Active Learning with Model Selection in Linear Regression.**[Citation Graph (, )][DBLP]**Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction.**[Citation Graph (, )][DBLP]**Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.**[Citation Graph (, )][DBLP]**Direct Density Ratio Estimation with Dimensionality Reduction.**[Citation Graph (, )][DBLP]**Influence-based collaborative active learning.**[Citation Graph (, )][DBLP]**Order Retrieval.**[Citation Graph (, )][DBLP]**A Framework of Adaptive Brain Computer Interfaces.**[Citation Graph (, )][DBLP]**Density Ratio Estimation: A New Versatile Tool for Machine Learning.**[Citation Graph (, )][DBLP]**Covariate shift adaptation for semi-supervised speaker identification.**[Citation Graph (, )][DBLP]**Geodesic Gaussian kernels for value function approximation.**[Citation Graph (, )][DBLP]**Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach.**[Citation Graph (, )][DBLP]**Mutual information estimation reveals global associations between stimuli and biological processes.**[Citation Graph (, )][DBLP]**Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method**[Citation Graph (, )][DBLP]
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