The SCEAS System
Navigation Menu

Search the dblp DataBase

Title:
Author:

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]


Search in 0.004secs, Finished in 0.005secs
NOTICE1
System may not be available sometimes or not working properly, since it is still in development with continuous upgrades
NOTICE2
The rankings that are presented on this page should NOT be considered as formal since the citation info is incomplete in DBLP
 
System created by asidirop@csd.auth.gr [http://users.auth.gr/~asidirop/] © 2002
for Data Engineering Laboratory, Department of Informatics, Aristotle University © 2002