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・学術論文（査読付き）
・Binary classification with pseudo exponential model and its application for multi task learning. T. Takenouchi, O. Komori and S. Eguchi. Entropy 17, 8, (2015) 5673-5694.
・Generalized t-statistics for two-group classification. O. Komori, S. Eguchi and J. Copas. Biometrics, 71, 2 (2015) 404-416.
・Robust Clustering Method in the Presence of Scattered Observations. A. Notsu and S. Eguchi. In press. Neural Computation, 2016.
・Risk assessment of radioisotope contamination for aquatic living resources in and around Japan. H. Okamura, S. Ikeda, T. Morita and S. Eguchi. Proceedings of the National Academy of Sciences 201519792, 2016.
・著書
・Statistical and machine-learning methods for class prediction in high dimension. O.Komori and S.Eguchi. Chapter 14 in Design and Analysis of Clinical Trials for Predictive Medicine. Eds S. Matsui, M. Buyse, R. Simon. Chapman Hall/CRC Biostatistics Series 2015.
・Path Connectedness on a Space of Probability Density Functions. Eguchi, S.and Komori, O. (2015). In Geometric Science of Information 2015 (pp. 615-624). Springer International Publishing.
・Spontaneous learning for data distributions via minimum divergence. S. Eguchi, A. Notsu and O. Komori. To appear as one chapter in Computational Information Geometry in Signal and Image Processing, Springer, 2016.
・口頭発表
・自発的なデータ学習の展望. 平成27年度 統計科学セミナー, 九州大学伊都キャンパス, 7月17日, 2015. (招待講演)
・自発的なデータ学習について．江口真透．統計関連学会連合大会2015, 岡山大学 津島キャンパス，9月7日，2015．
・分布の異質性を考慮したｔ統計量とAUCの一般化．小森理，江口真透．統計関連学会連合大会2015,
岡山大学 津島キャンパス，9月7日，2015．
・クラスタリングのためのロバストなK-平均法について．野津昭文，江口真透．統計関連学会連合大会2015, 岡山大学 津島キャンパス，9月7日，2015．
・異質性が内在する2標本検定問題におけるロバストな統計量．大前勝弘，小森理，江口真透．統計関連学会連合大会2015, 岡山大学 津島キャンパス，9月8日，2015．
・Spontaneous learning for data distributions via minimum divergence. ICMS Workshop on Computational information geometry for image and signal processing, September 21, 2015, Edinburgh. (Invited talk).
・Information geometry and spontaneous data learning. S. Eguchi. 2015 Workshop on High-Dimensional Statistical Analysis. December 2015, Academia Sinica, Taiwan. (Keynote talk).
・Spontaneous learning for clustered data via gamma power divergence. The 9th Conference of the Asian Regional Section of the IASC, December19, 2015, National University of Singapore, Singapore. (Invited talk).
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