Journal Papers and Conference Proceedings sorted by subjects (incomplete list)
Machine Learning and Robust Optimization

T. Kanamori, A. Takeda, T. Suzuki,
Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems.
Journal of Machine Learning Research. to appear.
 A. Takeda, H. Mitsugi, T. Kanamori,
A Unified Classification Model Based on Robust Optimization.
Neural Computation, to appear.

Takeda, A., Mitsugi, H., Kanamori, T.
A Unified Robust Classification Model.
29th International Conference on Machine Learning (ICML2012),
Edinburgh, Scotland, Jun. 26Jul. 1, 2012.

Kanamori, T., Takeda, A., Suzuki, T.
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems.
25th International Conference on Learning Theory (COLT2012),
Edinburgh, Scotland, Jun. 25Jun. 27, 2012.

Takeda A. Kanamori T., Mitsugi H.
Robust optimizationbased classification method.
The 21st International Symposium on Mathematical Programming
(ISMP 2012), Berlin, Germany, 1924 Aug., 2012.

A. Takeda, T. Kanamori,
A Robust Approach Based on Conditional ValueatRisk Measure to Statistical Learning Problems.
[site]
European Journal of Operational Research, 198, pp. 287296, 2009.
Nonlinear/Robust/Samplebased Optimization

T. Kanamori and A. Takeda.,
A Numerical Study of Learning Algorithms on Stiefel Manifold.
Computational Management Science, to appear.

T. Kanamori, A. Ohara,
A Bregman extension of quasiNewton updates II: analysis of robustness properties.
Journal of Computational and Applied Mathematics, to appear.

T. Kanamori, A. Ohara,
A Bregman Extension of quasiNewton updates I: An Information Geometrical framework.
[arXiv]
Optimization Methods and Software, vol. 28, issue 1, pp. 96123, 2013.

Kanamori T., Takeda A.
NonConvex Optimization on Stiefel Manifold and Applications to Machine Learning.
The 19th International Conference on Neural Information Processing (ICONIP 2012), Doha, Qatar, 1215 Nov., 2012.

T. Kanamori, A. Takeda,
WorstCase Violation of Sampled Convex Programs for Optimization with Uncertainty.
[arXiv]
Journal of Optimization Theory and Applications,
vol. 152, Issue 1, pp.171197, 2012.

Kanamori, T.
WorstCase Violation of Sampled Convex Programs for
Optimization with Uncertainty.
International Conference on Continuous Optimization, Hamilton,
Canada, 2007.

Kanamori, T. and Takeda, A.
WorstCase Violation of Sampled Convex Programs for Optimization with Uncertainty.
International Symposium on Mathematical Programming,
Dio de Janeiro, Brazil, 2006.
Density Ratio/Difference Estimation

T. Kanamori and M. Sugiyama,
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios.
Entropy, to appear.

M. Sugiyama, S. Liu, M. C. du Plessis, Y. Yamanaka, M. Yamada, T. Suzuki, T. Kanamori,
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning.
Journal of Computing Science and Engineering, to appear.

S. Sugiyama, T. Kanamori, T. Suzuki, M. C. du Plessis, S. Liu, I. Takeuchi,
Density Difference Estimation.
Neural Computation, to appear.

M. Kawakita, T. Kanamori,
SemiSupervised Learning with DensityRatio Estimation.
[arXiv]
Machine Learning, to appear.

M. Yamada, T. Suzuki, T. Kanamori, H. Hachiya, M. Sugiyama,
Relative DensityRatio Estimation for Robust Distribution Comparison.
Neural Computation, to appear.

Sugiyama M., Kanamori T., Suzuki T., Plessis M., Liu S., Takeuchi I.
DensityDifference Estimation.
The Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nevada, United States, 38 Dec., 2012.

T. Kanamori, T. Suzuki, M. Sugiyama,
Computational Complexity of KernelBased DensityRatio Estimation: A Condition Number Analysis.
[arXiv]
Machine Learning, vol. 90, pp. 431460, 2013.

T. Kanamori, T. Suzuki, M. Sugiyama,
Condition Number Analysis of Kernelbased Density Ratio Estimation.
ICML workshop on Numerical Mathematics in Machine Learning, Montreal Canada, June 2009.

M. Sugiyama, T. Suzuki, T. Kanamori,
Densityratio matching under the Bregman divergence: A unified framework of densityratio estimation.
[site]
Annals of the Institute of Statistical Mathematics, vol. 64, no. 5, pp. 10091044, 2012.

T. Kanamori, T. Suzuki, M. Sugiyama,
Statistical analysis of kernelbased leastsquares densityratio estimation.
[site]
Machine Learning, vol. 86, Issue 3, pp. 335367, 2012.

T. Kanamori, T. Suzuki, M. Sugiyama,
fdivergence estimation and twosample homogeneity test
under semiparametric densityratio models.
[arXiv]
IEEE Transactions on Information Theory, Vol. 58, Issue 2, pp. 708720, 2012.

Kanamori T., Suzuki, T., Sugiyama, M.
fdivergence estimation and twosample test under semiparametric density ratio models.
The 2nd Institute of Mathematical Statistics, Asia Pacific Rim Meeting (imsAPRM 2012),
Tsukuba, Japan, 24 July, 2012.

Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M.
Relative densityratio estimation for robust distribution comparison.
Presented at Neural Information Processing Systems (NIPS2011), Granada, Spain, Dec. 1315, 2011

M. Sugiyama, T. Suzuki, Y. Itho, T. Kanamori, M. Kimura,
LeastSquares TwoSample Test.
[site]
Neural Networks, vol. 24, pp. 735751, September, 2011.

S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, T. Kanamori,
Statistical Outlier Detection Using Direct Density Ratio
Estimation.
[site]
Knowledge and Information Systems, vol. 26, num. 2, pp. 309336,
August, 2011.

M. Sugiyama, M. Yamada, von Bunau P., T. Suzuki, T. Kanamori, M. Kawanabe,
Direct densityratio estimation with dimensionality reduction
via leastsquares heterodistributional subspace search.
[site]
Neural Networks, vol. 24, pp. 183198, March, 2011.

Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., & Kawanabe, M.,
Direct density ratio estimation with dimensionality reduction.
In S. Parthasarathy, B. Liu, B. Goethals, J. Pei, and C. Kamath
(Eds.), Proceedings of the 10th SIAM International Conference on Data
Mining (SDM2010), pp.595606, Columbus, Ohio, USA, Apr. 29May 1,
2010.

Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., & Okanohara, D.,
Conditional density estimation via leastsquares density ratio estimation.
In Proceedings of Thirteenth International Conference on Artificial
Intelligence and Statistics (AISTATS2010), JMLR Workshop and
Conference Proceedings, vol.9, pp.781788, Sardinia, Italy, May 1315,
2010.

T. Kanamori, T. Suzuki, M. Sugiyama,
Theoretical Analysis of Density Ratio Estimation.
[site]
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences,
vol. E93A, no. 4, pp. 787798, April, 2010.

M. Sugiyama, I. Takeuchi, T. Suzuki, T. Kanamori, H. Hachiya, D. Okanohara,
LeastSquares Conditional Density Estimation.
[site]
IEICE Transactions on Information and Systems, vol.E93D, no.3, pp.583594, March, 2010.

T. Kanamori
Efficient direct importance estimation for covariate shift
adaptation and outlier detection.
The 1st Institute of Mathematical Statistics, Asia Pacific Rim
Meeting, Seoul, June 28July 1, 2009

T. Kanamori, S. Hido, M. Sugiyama,
A Leastsquares Approach to Direct Importance Estimation.
[site]
Journal of Machine Learning Research. 10(Jul):13911445, 2009.
 Kanamori, T.
A Leastsquares Approach to Direct Importance Estimation and its Applications..
Joint Session of the CSA, JSS and KSS at 2008 Statistical Symposium,
China, Taipei, Dec. 19, 2008.

M. Sugiyama, T. Kanamori, T. Suzuki,
Shohei Hido, Jun Sese, Ichiro Takeuchi, and Liwei Wang,
A Densityratio Framework for Statistical Data Processing.
[site]
IPSJ Computer Vision and Application. vol. 1, pp. 183208, 2009

T. Suzuki, M. Sugiyama, T. Kanamori, and J. Sese,
Mutual information estimation reveals global associations
between stimuli and biological processes.
[site]
BMC Bioinformatics, vol. 10, no. 1, pp.S52, 2009.

Suzuki, Sugiyama, Kanamori, Sese,
Mutual Information Estimation Reveals Global Associations
between Stimuli and Biological Process.
The 7th Asia Pacific Bioinformatics Conference (APBC2009)
Beijing, China, 1316 January 2009.

Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T..
Inlierbased outlier detection via direct density ratio estimation.
Proceedings of IEEE International Conference on
Data Mining (ICDM2008), Pisa, Italy, Dec. 1519, 2008.

Takafumi Kanamori, Masashi Sugiyama, and Shohei Hido
Efficient Direct Density Ratio Estimation for Nonstationarity
Adaptation and Outlier Detection.
Presented at Neural Information Processing Systems (NIPS2008), Vancouver, Canada, 2008.

Taiji Suzuki, Masashi Sugiyama, Jun Sese, and Takafumi Kanamori.
A leastsquares approach to mutual information estimation
with application in variable selection.
Workshop on New Challenges for Feature Selection in Data Mining
and Knowledge Discovery 2008 (FSDM2008), Antwerp, Belgium,
Sep. 15, 2008

Suzuki, T., Sugiyama, M., Sese, J. and Kanamori, T.
Approximating mutual information by maximum likelihood density ratio estimation.
Proceedings of the Workshop on New Challenges
for Feature Selection in Data Mining and Knowledge Discovery 2008
(FSDM2008),
JMLR Workshop and Conference Proceedings, 2008.
Boosting

T. Kanamori, T. Takenouchi,
Improving LogitBoost with Prior Knowledge.
Information Fusion, vol. 14, pp. 208219, 2013.

T. Kanamori,
Deformation of LogLikelihood Loss Function for Multiclass Boosting.
[site]
Neural Networks, vol. 23, pp. 843864, May, 2010.

T. Takenouchi, S. Eguchi, N. Murata, T. Kanamori,
Robust Boosting Algorithm against Mislabelling in MultiClass Problems.
[site]
Neural Computation, vol. 20, num. 6, pp. 15961630, 2008.

Kanamori, T.
Multiclass Boosting Algorithms for Shrinkage Estimators of
Class Probability.
18th International Conference on Algorithmic Learning Theory,
Sendai International Center, Sendai, Japan, 2007.

T. Kanamori,
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability.
[site]
IEICE Transactions on Information and Systems, pp. 20332042, 2007.

T. Kanamori, , T. Takenouchi, S. Eguchi, N. Murata,
Robust Loss Functions for Boosting.
[site]
Neural Computation, 19(8), pp. 21832244, 2007

Kanamori T., Takenouchi, T., Eguchi, S., and Murata, N.
The most robust loss function for boosting.
Lecture Notes in Computer Science
Neural Information Processing: 11th International
Conference, ICONIP 2004, Calcutta, Vol. 3316, pp.496501,
Springer.

T. Kanamori, T. Takenouchi, N. Murata,
Geometrical Structure of Boosting Algorithm.
New Generation Computing,
Tutorial Series on BrainInspired Computing, Part 6,
25(1):117141, 2007.

Kanamori, T.
Integrability of weak learner on boosting.
The 2nd International Symposium on Information Geometry and
its Applications, pp. 300307, University of Tokyo, Tokyo, Japan, 2005.

N. Murata, T. Takenouchi, T. Kanamori, S. Eguchi,
Information Geometry of UBoost and Bregman Divergence.
Neural Computation, 16(7):14371481, July 2004.

Kanamori, T.
A New Sequential Algorithm for Regression Problems by using
Mixture Distribution.
In Proceedings of 2002 International Conference on
Artificial Neural Networks (ICANN'02),
pp. 535540, Madrid, Spain, August 2002.
Quantile Regression

I. Takeuchi, K. Nomura, T. Kanamori,
Nonparametric Conditional Density Estimation Using
PiecewiseLinear Path Following for Kernel Quantile Regression.
[site]
Neural Computation, vol. 21, num. 2, pp. 533559, 2009.

T. Kanamori, and I. Takeuchi,
Conditional Mean Estimation under Asymmetric and
Heteroscedastic Error by Linear Combination of Quantile
Regressions.
Computational Statistics and Data Analysis,
Vol 50, Issue 12, pp 36053618, 2006.

Takeuchi, I., Nomura, K. and Kanamori, T.
The Entire Solution Path of Kernelbased Nonparametric
Conditional Quantile Estimator.
International Joint Conference on Neural Networks,
Vancouver, Canada, 2006,

Kanamori, T. and Takeuchi, I.
Estimators for Conditional Expectations under Asymmetric and
Heteroscedastic Error Distributions.
International Symposium on The Art of Statistical Metaware,
The Institute of Statistical Mathematics, Tokyo, Japan, 2005.

Kanamori T. and Takeuchi, I.
Robust Estimation of Conditional Mean by the Linear
Combination of Quantile Regressions.
International Conference on Robust Statistics,
Beijing, China, 2004.

I. Takeuchi, Y. Bengio, T. Kanamori,
Robust Regression with Asymmetric HeavyTail Noise
distributions.
Neural Computation, Vol. 14, Num. 10, pp. 24692496, 2002.

Bengio, Y., Takeuchi, I. and Kanamori, T.
The Challenge of NonLinear Regression on Large Datasets with
Asymmetric Heavy Tails.
In Proceedings of the Joint Statistical Meeting. American
Statistical Association, New York, U.S.A., August 2002.
Active Learning

T. Kanamori,
Poolbased Active Learning with Optimal Sampling
Distribution and its Information Geometrical
Interpretation.
[site]
Neurocomputing, Vol. 71, Issue 13, pp. 353362, 2007.

T. Kanamori, H. Shimodaira,
Active Learning algorithm using the maximum weighted
loglikelihood estimator
Journal of Statistical Planning and Inference,
Vol. 116, Issue 1, pp. 149162, 2003.

T. Kanamori,
Statistical Asymptotic Theory of Active Learning.
Annals of the Institute of Statistical Mathematics,
Vol. 54, Num. 3, pp. 459475, 2002.

T. Kanamori, H. Shimodaira,
An Active Learning Algorithm Using an Information Criterion for the
Maximum Weighted Loglikelihood Estimator.
Proceedings of the Institute of Statistical Mathematics, Vol, 48, No. 1, 197212,
2000.

Shimodaira, H., and Kanamori, T.
Information Criteria for Predictive Inference with the
Weighted LogLikelihood and the Active Learning.
International Society for Bayesian Analysis,
Sixth World Meeting Hersonissos, Heraklion, Crete, May 2000.

T. Kanamori,
Active Learning Algorithm using Maximum Weighted Likelihood Estimator.
Bulletin of the Computational Statistics in Japan, vol. 11, Num. 2, pp. 6575, 1998.
Statistics & Learning Theory

T. Kanamori and H. Fujisawa,
Affine Invariant Divergences associated with Proper Composite Scoring Rules and their Applications.
Bernoulli, to appear.

M. Kawakita, T. Kanamori,
SemiSupervised Learning with DensityRatio Estimation.
[arXiv]
Machine Learning, to appear.

T. Kanamori,
Statistical Models and Learning Algorithms for Ordinal Regression Problems.
Information Fusion, vol. 14, pp. 199207, 2013.

T. Kanamori, H. Uehara, M. Jimbo,
Pooling Design and Bias Correction in DNA Library Screening.
[arXiv]
Journal of Statistical Theory and Practice, vol. 6, issue 1, pp. 220238, 2012.

H. Shimodaira, T. Kanamori, M. Aoki, K. Mine,
Multiscale Bagging and its Applications.
[site]
IEICE Transactions on Information and Systems, Volume E94D No.10, pp.19241932, 2011.

Shimodaira H.Kanamori T., Masayoshi A., Kouta Mine
Multiscale Bagging with Applications to Classification and Active Learning.
The 2nd Asian Conference on Machine Learning, Nov. 2010.

A. Masayoshi, Kanamori T., Shimodaira H
Multiscalebagging with Applications to Classification.
The 2nd Asian Conference on Machine Learning, Nov. 2010.
kanamori's web site