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Masashi Sugiyama: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. Masashi Sugiyama, Klaus-Robert Müller
    Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:528-534 [Conf]
  6. Masashi Sugiyama, Klaus-Robert Müller
    Model Selection Under Covariate Shift. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:235-240 [Conf]
  7. Masashi Sugiyama
    Local Fisher discriminant analysis for supervised dimensionality reduction. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:905-912 [Conf]
  8. Masashi Sugiyama, Hidemitsu Ogawa
    Incremental Active Learning with Bias Reduction. [Citation Graph (0, 0)][DBLP]
    IJCNN (1), 2000, pp:15-20 [Conf]
  9. 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]
  10. 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]
  11. Masashi Sugiyama
    Active Learning for Misspecified Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. Amos J. Storkey, Masashi Sugiyama
    Mixture Regression for Covariate Shift. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1337-1344 [Conf]

  28. Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. [Citation Graph (, )][DBLP]


  29. Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise. [Citation Graph (, )][DBLP]


  30. On the Margin Explanation of Boosting Algorithms. [Citation Graph (, )][DBLP]


  31. Inlier-Based Outlier Detection via Direct Density Ratio Estimation. [Citation Graph (, )][DBLP]


  32. nu-support vector machine as conditional value-at-risk minimization. [Citation Graph (, )][DBLP]


  33. Implicit Regularization in Variational Bayesian Matrix Factorization. [Citation Graph (, )][DBLP]


  34. Nonparametric Return Distribution Approximation for Reinforcement Learning. [Citation Graph (, )][DBLP]


  35. A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. [Citation Graph (, )][DBLP]


  36. Least absolute policy iteration for robust value function approximation. [Citation Graph (, )][DBLP]


  37. Estimating Squared-Loss Mutual Information for Independent Component Analysis. [Citation Graph (, )][DBLP]


  38. Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. [Citation Graph (, )][DBLP]


  39. Multi-Task Learning via Conic Programming. [Citation Graph (, )][DBLP]


  40. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. [Citation Graph (, )][DBLP]


  41. Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. [Citation Graph (, )][DBLP]


  42. Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. [Citation Graph (, )][DBLP]


  43. Analysis of Variational Bayesian Matrix Factorization. [Citation Graph (, )][DBLP]


  44. Pool-Based Agnostic Experiment Design in Linear Regression. [Citation Graph (, )][DBLP]


  45. Efficient Sample Reuse in EM-Based Policy Search. [Citation Graph (, )][DBLP]


  46. Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information. [Citation Graph (, )][DBLP]


  47. Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. [Citation Graph (, )][DBLP]


  48. Integration of Multiple Networks for Robust Label Propagation. [Citation Graph (, )][DBLP]


  49. Active Learning with Model Selection in Linear Regression. [Citation Graph (, )][DBLP]


  50. Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. [Citation Graph (, )][DBLP]


  51. Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. [Citation Graph (, )][DBLP]


  52. Direct Density Ratio Estimation with Dimensionality Reduction. [Citation Graph (, )][DBLP]


  53. Influence-based collaborative active learning. [Citation Graph (, )][DBLP]


  54. Order Retrieval. [Citation Graph (, )][DBLP]


  55. A Framework of Adaptive Brain Computer Interfaces. [Citation Graph (, )][DBLP]


  56. Density Ratio Estimation: A New Versatile Tool for Machine Learning. [Citation Graph (, )][DBLP]


  57. Covariate shift adaptation for semi-supervised speaker identification. [Citation Graph (, )][DBLP]


  58. Geodesic Gaussian kernels for value function approximation. [Citation Graph (, )][DBLP]


  59. Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. [Citation Graph (, )][DBLP]


  60. Mutual information estimation reveals global associations between stimuli and biological processes. [Citation Graph (, )][DBLP]


  61. Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method [Citation Graph (, )][DBLP]


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