論文

• Simm, J., Sugiyama, M., Hachiya, H.. Multi-task approach to reinforcement learning for factored-state Markov decision problems, IEICE, IEICE Transactions on Information and Systems, vol. E95-D, no. 10, pp. 2426-2437, 2012.
• Yamada, M., Sugiyama, M., Wichern, G., Simm, J.. Direct importance estimation with a mixture of probabilistic principal component analyzers, IEICE Transactions on Information and Systems,, Vol. E93-D, no. xxx, pp. xxx-xxx, 2010.

国際会議発表 (査読有り)

• Simm, J., Sugiyama, M., Kato, T.. Computationally efficient multi-task learning with least-squares probabilistic classifiers., IPSJ Transactions on Computer Vision and Applications, vol. 3, pp. 1－8, 2011.
• Sugiyama, M., Simm, J.. A computationally-efficient alternative to kernel logistic regression., IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010),, In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010),, pp. 124-129, Aug. 2010.
• Simm, J., Sugiyama, M., Hirotaka Hachiya. Improving model-based reinforcement learning with multitask learning., IPSJSIGMathematicalModellingandProblemSolving,, IPSJSIGTechnicalReport,, Vol. 2009-MPS-76, no. 3, pp. 1-8, Dec. 2009.
• Simm, J., Sugiyama, M., Hachiya, H.. Observational reinforcement learning., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Proceedings of 2009 Workshop on Information-Based Induction Sciences (IBIS2009),, Oct. 2009.

国内会議発表 (査読有り)

• Simm, J., Sugiyama, M., Kato, T.. Multi-task learning with least-squares probabilistic classifiers., IBISML2010, IEICE Technical Report, IBISML2010-32, pp. 51-56, Sept. 2010.

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