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ジャーナル論文 / ジャーナル論文(日本語) / 国際会議論文

ジャーナル論文

  1. Y. Yamaguchi, T. Atsumi, K. Kanamori, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama and I. Takeuchi, Drawing a materials map with an autoencoder for lithium ionic conductors, Scientific Reports, vol.13, 16799, 2023.
  2. T. Otsuka, R. Oka, M. Karasuyama, T. Hayakawa, Photoluminescence Color Prediction for Eu3+-doped Perovskite Red Phosphors using Machine Learning, Physica Status Solidi - Rapid Research Letters, 2300237, 2023.
  3. K. Matsunoshita, Y. Yamaguchi, M. Hamaie, M. Horibe, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, R. Kobayashi, Optimization of Force-Field Potential Parameters Using Conditional Variational Autoencoder, Science and Technology of Advanced Materials: Methods, vol.3, no.1, 2253713, 2023.
  4. H. Fukuda, S. Kusakawa, K. Nakano, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, I. Takeuchi, T. Natorie and Y. Ono, Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solid-state Li-metal batteries, RSC Advances, vol.12, no.47, pp.30696-30703, 2022.
  5. H. Hirai, T. Iizawa, T. Tamura, M. Karasuyama, R. Kobayashi, and T. Hirose, Machine-learning-based prediction of first-principles XANES spectra for amorphous materials, Physical Review Materials, vol.6, no.16, 115601, 2022.
  6. S. Kusakawa, S. Takeno, Y. Inatsu, K. Kutsukake, S. Iwazaki, T. Nakano, T. Ujihara, M. Karasuyama, I. Takeuchi, Bayesian Optimization for Cascade-type Multistage Processes, Neural Computation, vol.34, no.12, pp.2408-2431, 2022.
  7. M. Nakayama, K. Nakano, M. Harada, N. Tanibata, T. Hayami, Y. Noda, R. Kobayashi, M. Karasuyama, I. Takeuchi and M. Kotobuki, Na Superionic Conductor-Type LiZr2(PO4)3 as a Promising Solid Electrolyte for Use in All-Solid-State Li Metal Batteries, Chemical Communications, vol.58, no.67, pp.9328-9340, 2022.
  8. S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M Shiga, I. Takeuchi and Masayuki Karasuyama, A Generalized Framework of Multi-fidelity Max-value Entropy Search through Joint Entropy, Neural Computation, vol.34, no.10, pp.2145-2203, 2022.
  9. T. Tsukurimichi, Y. Inatsu, V. N. L. Duy and I. Takeuchi. Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation. Annals of the Institute of Statistical Mathematics, 74, 1197–1228, 2022.
  10. T. Atsumi, K. Sato, Y. Yamaguchi, M. Hamaie, R. Yasuda, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, I. Takeuchi, Chemical composition data‐driven machine‐learning prediction for phase stability and materials properties of inorganic crystalline solids. physica status solidi (b) 2022.
  11. S. Iwazaki, Y. Inatsu, I. Takeuchi Bayesian Quadrature Optimization for Probability Threshold Robustness Measure. Neural Computation, 33(12), 3413-3466, 2021.
  12. T. Yoshida, I. Takeuchi, M. Karasuyama Distance Metric Learning for Graph Structured Data. Machine Learning vol.110, no.7, 1765-1811, 2021.
  13. V.N.L. Duy, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, H. Hanada,Y. Tabei, I. Takeuchi Stat-DSM: Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction. IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 3, pp. 1477-1488, 2022.
  14. Z. Yang, S. Suzuki, N. Tanibata,H. Takeda, M. Nakayama, M. Karasuyama, I. Takeuchi An Efficient Experimental Search for Discovering a Fast Li Ion Conductor from Perovskite-type LixLa(1-x)/3NbO3 (LLNO) Solid State Electrolyte Using Bayesian Optimization. The Journal of Physical Chemistry C: vol.125, no.1, pp.152-160, 2021.
  15. K. Ueno, K. Ichikawa,K. Sato ,D. Sugita, S. Yotsuhashi, I. Takeuchi Robust and efficient calculation of activation energy by automated path search and density functional theory. Physical Review Materials: vol. 5, 033801 2021.
  16. K. Inoue, M. Karasuyama, R. Nakamura, M. Konno, D. Yamada, K. Mannen, T. Nagata, Y. Inatsu,H. Yawo,K. Yura,O. Béjà, H. Kandori, I. Takeuchi Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design. Communication Biology 4, Article number: 362 2021.
  17. S. Suzumura,K. Nakagawa, Y. Umezu, K. Tsuda, I. Takeuchi Selective inference for high-order interaction features selected in a stepwise manner. IPSJ Transactions on Bioinformatics: vol.14, pp.1-11 2021.
  18. T. Tamura, and M. Karasuyama Prediction of formation energies of large-scale disordered systems via active-learning based executions of ab initio local-energy calculations: a case study on a Fe random grain boundary model with millions of atoms. Physical Review Materials. vol.4, no.11, 113602, 2020.
  19. S. Takeno,Y. Tsukada, H. Fukuoka,T. Koyama,, M. Shiga, and M. Karasuyama Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling. Physical Review Materials. vol.4, no.8, 083802, 2020.
  20. M. Karasuyama, H. Kasugai, T. Tamura, and K. Shitara Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and Lithium. Computational Materials Science. vol.184, 109927, 2020.
  21. K. Osada,K. Kutsukake,J. Yamamoto,S. Yamashita,T. Kodera,Y. Nagai,T. Horikawa, K. Matsui,I. Takeuchi,T. Ujihara Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints. Materials Today Communication. vol.25, 101538, 2020 Dec.
  22. S. Iwazaki, Y. Inatsu, I. Takeuchi Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty IEEE Access. vol.8, pp.203982-203993 ,2020 Nov.
  23. Y. Inatsu, M. Karasuyama, K. Inoue, I. Takeuchi Active learning for level set estimation under input uncertainty and its extensions. Neural Computation: vol.32, pp.2486-2531, 2020 Dec.
  24. M. Harada,H. Takeda, S. Suzuki, K. Nakano, N. Tanibata, M. Nakayama, M. Karasuyama, I. Takeuchi Bayesian-optimization-guided Experimental Search of NASICON-type Solid Electrolytes for All-solid-state Li-ion Batteries. Journal of Materials Chemistry A: vol.2020-8, pp.15103-15109, 2020 Jul.
  25. Y. Inatsu, M. Karasuyama, K. Inoue, H. Kandori, I. Takeuchi Active Learning of Bayesian Linear Models with High Dimensional Binary Features by Parameter Confidence-Region Estimation. Neural Computation: vol.32, pp.1998-2031, 2020 Oct.
  26. Y. Inatsu,D. Sugita, K. Toyoura, I. Takeuchi Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives. Neural Computation: vol.32, pp.2032-2068, 2020 Oct.
  27. K. Shinjo, K. Hara, G. Nagae, T. Umeda, K. Katsushima, M. Suzuki, Y. Murofushi, Y. Umezu, I. Takeuchi, S. Takahashi,Y. Okuno, K. Matsuo, H. Ito, S. Tajima, H. Aburatani, K. Yamao, Y. Kondo A Novel Sensitive Detection Method for DNA Methylation in Circulating Free DNA of Pancreatic Cancer. Plos One: vol.15-6: e0233782, 2020 Jun.
  28. K. Toyoura, T. Fujii, K. Kanamori, I. Takeuchi A Sampling Strategy in Efficient Potential Energy Surface Mapping for Predicting Atomic Diffusivity in Crystals by Machine Learning. Physical Review B: vol.101, pp.184117, 2020 May.
  29. K. Nakano, Y. Noda, N. Tanibata, M. Nakayama, R. Kobayashi, I. Takeuchi Exhaustive and Informatics-Aided Search for Fast Li-Ion Conductor with NASICON-Type Structure Using Material Simulation and Bayesian Optimization. APL Materials: vol.8, 041112. Published Online: 2020 Apr.
  30. T. Yoshida, I. Takeuchi, and M. Karasuyama Safe Triplet Screening for Distance Metric Learning. Neural Computation, vol.31, no.12, pp.2432-2491, 2019 Dec.
  31. Y. Umezu and I. Takeuchi Selective inference via marginal screening for high dimensional classification. Japanese Journal of Statistics and Data Science, vol.2, no.2, pp.559-589, 2019 Dec.
  32. Y. sukada,S. Takeno, M. Karasuyama, H. Fukuoka, M. Shiga, and T. Koyama Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling. Scientific Reports, vol.9, 15794, 2019 Oct.
  33. K. Matsui, W. Kumagai, K. Kanamori, M. Nishikimi, and T. Kanamori Variable Selection for Nonparametric Learning with Power Series Kernels. Neural Computation, vol.31, no.8, pp.1718-1750, 2019 Aug.
  34. T. Sakuma, K. Nishi, K. Kishimoto,K. Nakagawa, M. Karasuyama, Y. Umezu, S. Kajioka, S.J. Yamazaki, K.D. Kimura, S. Matsumoto, K. Yoda, M. Fukutomi, H. Shidara, H. Ogawa, and I. Takeuchi Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis. Advanced Robotics, vol.33, pp.134-152, 2019 Jan.
  35. T. Yonezu, T. Tamura, I. Takeuchi, and M. Karasuyama Knowledge-transfer-based cost-effective search for interface structures: A case study on fcc-Al [110] tilt grain boundary. Physical Review Materials, vol.2, 113802, 2018 Nov.
  36. M. Karasuyama, K. Inoue, R. Nakamura, H. Kandori, and I. Takeuchi Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach. Scientific Reports, vol.8, 15580, 2018 Oct.
  37. T. Hirakawa, T. Yamashita, T. Tamaki, H. Fujiyoshi, Y. Umezu, I. Takeuchi, S. Matsumoto, and K. Yoda Can AI predict animal movements? Filling gaps in animal trajectories using Inverse Reinforcement Learning. Ecosphere, vol.9, no.10, e02447, 2018 Oct.
  38. R. Jalem, K. Kanamori, I. Takeuchi, M. Nakayama, H. Yamasaki, and T. Saito Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application. Scientific Reports, vol.8, 5845, 2018 Apr.
  39. Y. Yasukochi, J. Sakuma, I. Takeuchi, K. Kato, M. Oguri, T. Fujimaki, H. Horibe, and Y. Yamada Identification of CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population. Molecular Genetics and Genomics, vol.293, no.2, pp.371-379, 2018 Apr.
  40. K. Kanamori, K. Toyoura, J. Honda, K. Hattori, A. Seko, M. Karasuyama, K. Shitara, M. Shiga, A. Kuwabara, and I. Takeuchi Exploring a potential energy surface by machine learning for characterizing atomic transport. Physical Review B, vol.97, 125124, 2018 Mar.
  41. K. Aoki, H. Nakamura, H. Suzuki, K. Matsuo, K. Kataoka, T. Shimamura, K. Motomura, F. Ohka, S. Shiina, T. Yamamoto, Y. Nagata, T. Yoshizato, M. Mizoguchi, T. Abe, Y. Momii, Y. Muragaki, R. Watanabe, I. Ito, M. Sanada, H. Yajima, N. Morita, I. Takeuchi, S. Miyano, T. Wakabayashi, S. Ogawa, and A. Natsume Prognostic relevance of genetic alterations in diffuse lower-grade gliomas. Neuro-Oncology, vol.20, no.1, pp.66-77, 2018 Jan.
  42. M. Gönen, B.A. Weir, G.S. Cowley, Y. Guan, A. Jaiswal, M. Karasuyama, V. Uzunangelov, F. Vazquez, T. Wang, A. Airola,A. Bivol,J. Boehm,K. Bunte,D. Carlin,S. Chopra,A. Deran,K. Ellrott,P. Gopalacharyulu,K. Graim,B. Hoff,S. Howell,S. Kaski,S.A. Khan,D. Marbach,Y. Newton,T.C. Norman,S. Ng,T. Pahikkala,E. Paull,A. Sokolov,H. Tang,J. Tang,A. Tsherniak,K. Wennerberg,Y. Xie,X. Zhan,F. Zhu, Broad-DREAM Community,T. Aittokallio,H. Mamitsuka,D. Root,J.M. Stuart,G. Xiao,G. Stolovitzky,W.C. Hahn, and A.A. Margolin A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Systems, vol.5, no.5, pp.485-497, 2017 Nov.
  43. T. Tamura, M. Karasuyama, R. Kobayashi, R. Arakawa, Y. Shiihara, and I. Takeuchi Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations. Modelling and Simulation in Materials Science and Engineering, vol.25, no.7, 075003, 2017 Aug.
  44. S. Suzumura, K. Ogawa, M. Karasuyama, M. Sugiyama, and I. Takeuchi Homotopy continuation approaches for robust SV classification and regression. Machine Learning, vol.106, no.7, pp.1009-1038, 2017 Jul.
  45. S. Yotsukura, M. Karasuyama, I. Takigawa, and H. Mamitsuka Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations: A Modicum in the Intrinsic Code. Briefings in Bioinformatics, vol.18, no.4, pp.619-633, 2017 Jul.
  46. M. Oguri, T. Fujimaki, H. Horibe, K. Kato, K. Matsui, I. Takeuchi, and Y. Yamada, Obesity-related changes in clinical parameters and conditions in a longitudinal population-based epidemiological study. Obesity Research and Clinical Practice, vol.11, no.3, pp.299-314, 2017 May-Jun.
  47. M. Karasuyama and H. Mamitsuka Adaptive Edge Weighting for Graph-Based Learning Algorithms. Machine Learning, vol.106, no.2, pp.307-335, 2017 Feb.
  48. Y. Murakami-Tonami, H. Ikeda, R. Yamagishi, M. Inayoshi, S. Inagaki, S. Kishida, Y. Komata, J. Koster, I. Takeuchi, Y. Kondo, T. Maeda, Y. Sekido, H. Murakami, and K. Kadomatsu SGO1 is involved in the DNA damage response in MYCN-amplified neuroblastoma cells. Scientific Reports, vol.6, 31615, 2016 Aug.
  49. N. Hijiya, Y. Tsukamoto, C. Nakada, N. Tung, T. Kai, K. Matsuura, K. Shibata, M. Inomata, T. Uchida, A. Tokunaga, K. Amada, K. Shirao, Y. Yamada, H. Mori, I. Takeuchi, M. Seto, M. Aoki, M. Takekawa, and M. Moriyama Genomic loss of DUSP4 contributes to the progression of intraepithelial neoplasm of pancreas to invasive carcinoma. Cancer Research, vol.76, no.9, pp.2612-2625, 2016 May.
  50. K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara, and I. Takeuchi Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides. Physical Review B, vol.93, 054112, 2016 Feb.
  51. T. Narimatsu, K. Matsuura, C. Nakada, Y. Tsukamoto, N. Hijiya, T. Kai, T. Inoue, T. Uchida, T. Nomura, F. Sato, M. Seto, I. Takeuchi, H. Mimata, and M. Moriyama Downregulation of NDUFB6 due to 9p24.1-p13.3 loss is implicated in metastatic clear cell renal cell carcinoma. Cancer Medicine, vol.4, pp.112-124, 2015 Jan.
  52. K. Tanahashi, A. Natsume, F. Ohka, H. Momota, A. Kato, K. Motomura, N. Watabe, S. Muraishi, H. Nakahara, Y. Saito, I. Takeuchi, and T. Wakabayashi Assessment of tumor cells in a mouse model of diffuse infiltrative glioma by Raman spectroscopy. BioMed Research International, vol.2014, 860241, 2014 Aug.
  53. F. Matsuoka, I. Takeuchi, H. Agata, H. Kagami, H. Shiono, Y. Kiyota, H. Honda, and R. Kato Characterization of time-course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells. Biotechnology and Bioengineering, vol.111, no.7, pp.1430-1439, 2014 Jul.
  54. M. Suguro, N. Yoshida, A. Umino, H. Kato, H. Tagawa, M. Nakagawa, N. Fukuhara, S. Karnan, I. Takeuchi, T.D. Hocking, K. Arita, K. Karube, S. Suzuki, S. Nakamura, T. Kinoshita, and M. Seto, Clonal heterogeneity of lymphoid malignancies correlates with poor prognosis. Cancer Science, vol.105, no.7, pp.897-904, 2014 Jul.
  55. H. Sasaki, I. Takeuchi, M. Okada, R. Sawada, K. Kanie, Y. Kiyota, H. Honda, and R. Kato Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells. PLoS ONE, vol.9, no.4, e93952, 2014 Apr.
  56. N. Isu, T. Hasegawa, I. Takeuchi, and A. Morimoto Quantitative analysis of time-course development of motion sickness by in-vehicle video watching. Display, vol.35, no.2, pp.90-97, 2014 Apr.
  57. Y. Guo, I. Takeuchi, S. Karnan, T. Miyata, K. Ohshima, and M. Seto, Array CGH profiling of immunohistochemical subgroups of diffuse large B-cell lymphoma shows distinct genomic alterations. Cancer Science, vol.105. no.4, pp.481-489, 2014 Apr.
  58. Y. Murakami-Tonami, S. Kishida, I. Takeuchi, Y. Katou, J. M. Maris, H. Ichikawa, Y. Kondo, Y. Sekido, K. Shirahige, H. Murakami, and K. Kadomatsu Inactivation of SMC2 shows a synergistic lethal response in MYCN-amplified neuroblastoma cells. Cell Cycle, vol.13, no.7, pp.1-17, 2014 Apr.
  59. J. Chang, S. Oikawa, H. Iwahashi,E. Kitagawa, I. Takeuchi, M. Yuda, C. Kato, Y. Yamada, G. Ichihara, M. Kato, and S. Ichihara Expression of proteins associated with adipocyte lipolysis was significantly changed in the adipose tissues of the obese spontaneously hypertensive/NDmcr-cp rat. Diabetology & Metabolic Syndrome, vol.6, no.1, pp.1-9, 2014 Jan.
  60. D. duVerle, I. Takeuchi, Y. Murakami-Tonami, K. Kadomatsu, and K. Tsuda Discovering Combinatorial Interactions in Survival Data. Bioinformatics, vol.29, no.23, pp.3053-3059, 2013 Dec.
  61. M. Sugiyama, T. Kanamori, T. Suzuki, M.C. du Plessis, S. Liu, and I. Takeuchi Density-Difference Estimation. Neural Computation, vol.25, no.10, pp.2734-2775, 2013 Oct.
  62. A. Natsume, M. Ito, K. Katsushima, F. Ohka, A. Hatanaka, K. Shinjo, S. Sato, S. Takahashi, Y. Ishikawa, I. Takeuchi, H. Shimogawa, M. Uesugi, H. Okano, S. Kim, T. Wakabayashi, I. Jean-Pierre, Y. Sekido, and Y. Kondo Chromatin regulator PRC2 is a key regulator of epigenetic plasticity in glioblastoma. Cancer Research, vol.73, no.14, pp.4559-4570, 2013 Jul.
  63. F. Matsuoka, I. Takeuchi, H. Agata, H. Kagami, H. Shiono, Y. Kiyota, H. Honda, and R. Kato Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells. PLoS ONE, vol.8, no.2, e55082, 2013 Feb.
  64. S. Yoshioka, Y. Tsukamoto, N. Hijiya, C. Nakada, T. Uchida, K. Matsuura, I. Takeuchi, M. Seto, K. Kawano, and M. Moriyama Genomic profiling of oral squamous cell carcinoma by array-based comparative genomic hybridization. PLoS ONE, vol.8, no.2, e56165, 2013 Feb.
  65. M. Karasuyama, N. Harada, M. Sugiyama, and I. Takeuchi Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines. Machine Learning, vol.88, no.3, pp.297-330, 2012 Sep.
  66. J. Chang, S. Oikawa, G. Ichihara, Y. Nanpei, Y. Hotta, Y. Yamada, S. Tada-Oikawa, H. Iwahashi, E. Kitagawa, I. Takeuchi, M. Yuda, and S. Ichihara Altered gene and protein expression in liver of the obese spontaneously hypertensive/NDmcr-cp rat. Nutrition and Metabolism, vol.9, 87, 2012 Sep.
  67. K. Shinjo, Y. Okamoto, B. An, YoT. Koyama, I. Takeuchi, M. Fujii, H. Osada, N. Usami, Y. Hasegawa, H. Ito, T. Hida, N. Fujimoto, T. Kishimoto, Y. Sekido, and Y. Kondo Integrated analysis of genetic and epigenetic alterations reveals CpG island methylator phenotype associated with distinct clinical characters of lung adenocarcinoma. Carcinogenesis, vol.33, no.7, pp.1277-1285, 2012 Jul.
  68. Y. Okamoto, A. Ito, S. Sawaki, T. Nishida, T. Takahashi, M. Toyota, H. Suzuki, Y. Shinomura, I. Takeuchi, K. Shinjo, B. An, H. Ito, K. Yamao, M. Fujii, H. Murakami, H. Osada, H. Kataoka, T. Joh, Y. Sekido, and Y. Kondo Aberrant DNA methylation associated with aggressiveness of gastrointestinal stromal tumor. Gut, vol.61, no.3, pp.392-401, 2012 Mar.
  69. Y. Kishida, A. Natsume, Y. Kondo, I. Takeuchi, B. An, Y. Okamoto, K. Shinjo, K. Saito, H. Ando, F. Ohka, Y. Sekido, and T. Wakabayashi, Epigenetic subclassification of meningiomas based on genome-wide DNA methylation analyses. Carcinogenesis, vol.32, no.2, pp.436-441, 2012 Feb.
  70. K. Matsuura, C. Nakada, M. Mashio, T. Narimatsu, T. Yoshimoto, M. Tanigawa, Y. Tsukamoto, N. Hijiya, I. Takeuchi, T. Nomura, F. Sato, H. Mimata, M. Seto, and M. Moriyama Downregulation of SAV1 plays a role in pathogenesis of high-grade clear cell renal cell carcinoma. BMC Cancer, vol.11, 523, 2011 Dec.
  71. M. Karasuyama and I. Takeuchi Nonlinear Regularization Path for Quadratic Loss Support Vector Machines. IEEE Transactions on Neural Networks, vol.22, no.10, pp.1613-1625, 2011 Oct.
  72. K. Karube, M. Nakagawa, TS. Suzuki, I. Takeuchi, K. Honma, Y. Nakashima, N. Shimizu, Y.H. Ko, Y. Morishima, K. Ohshima, S. Nakamura, and M. Seto Identification of FOXO3 and PRDM1 as tumor suppressor gene candidates in NK cell neoplasms by genomic and functional analyses. Blood, vol.118, no.12, pp.3195-3204, 2011 Sep.
  73. A. Kuroda, Y. Tsukamoto, L.T. Nguyen, T. Noguchi, I. Takeuchi, M. Uchida, T. Uchida, N. Hijiya, C. Nakada, T. Okimoto, M. Kodama, K. Murakami, K. Matsuura, M. Seto, H. Ito, T. Fujioka, and M. Moriyama Genomic profiling of submucosal-invasive gastric cancer by array-based comparative genomic hybridization. PLoS ONE, vol.6, no.7, e22313, 2011 Jul.
  74. P. Huang,, S. Kishida, D. Cao, Y. Murakami-Tonami, P. Mu, M. Nakaguro, N. Koide, I. Takeuchi, A. Onishi, and K. Kadomatsu NeuroD1 Downregulates Slit2 Expression and Promotes Cell Motility and Tumor Formation of Neuroblastoma. Cancer Research, vol.71, no.8, pp.2938-2948, 2011 Apr.
  75. H. Ju, B. An, Y. Okamoto, K. Shinjo, Y. Kanemitsu, K. Komori, T. Hirai, Y. Shimizu, T. Sano, A. Sawaki, M. Tajika, K. Yamao, M. Fujii, H. Murakami, H. Osada, H. Ito, I. Takeuchi, Y. Sekido, and Y. Kondo, Distinct Profiles of Epigenetic Evolution between Colorectal Cancers with and without Metastasis. American Journal of Pathology, vol.178, no.4, pp.1835-1846, 2011 Mar.
  76. Y. Ishikawa, and I. Takeuchi Differentially Aberrant Region Detection in Array CGH Data based on Nearest Neighbor Classification Performance. IPSJ Transactions on Bioinformatics, vol.3, pp.70-81, 2010 Oct.
  77. M. Karasuyama and I. Takeuchi Multiple Incremental Decremental Learning of Support Vector Machines. IEEE Transactions on Neural Networks, vol.21, no.7, pp.1048-1059, 2010 Jun.
  78. M. Uchida, Y. Tsukamoto, T. Uchida, Y. Ishikawa, T. Nagai, N. Hijiya, N. Tung, C. Nakada, A. Kuroda, T. Okimoto, M. Kodama, K. Murakami, T. Noguchi, K. Matsuura, M. Tanigawa, M. Seto, H. Ito, T. Fujioka, I. Takeuchi, and M. Moriyama Genomic profiling of gastric carcinoma in situ and adenomas by array-based comparative genomic hybridization. Journal of Pathology, vol.221, no.1, pp.96-105, 2010 May.
  79. Y. Ishikawa, I. Takeuchi, and R. Nakano Multi-directional search from the primitive initial point for Gaussian mixture estimation using variational Bayes method. Neural Networks, vol.23, no.3, pp.356-364, 2010 Apr.
  80. M. Sugiyama, I. Takeuchi, T. Suzuki, T. Kanamori, H. Hachiya, and D. Okanohara Least-Squares Conditional Density Estimation. IEICE Transactions on Information and Systems, vol.E93-D, no.3, pp.583-594, 2010 Mar.
  81. M. Karasuyama, I. Takeuchi, and R. Nakano Efficient leave-m-out cross-validation of support vector regression by generalizing decremantal algorithm. New Generation Computing, vol.27, no.4, pp.307-318, 2009 Nov.
  82. M. Sugiyama, T. Kanamori,, T. Suzuki, S. Hido, J. Sese, I. Takeuchi, and L. Wang A density-ratio framework for statistical data processing. IPSJ Transactions on Computer Vision and Applications, vol.1, pp.183-208, 2009 Sep.
  83. H. Moriguchi, I. Takeuchi, M. Karasuyama, S. Horikawa, Y. Ohta, T. Kodama, and H. Naruse Adaptive kernel quantile regression for anomaly detection. Journal of Advanced Computational Intelligence and Inteligent Informatics, vol.13, no.3, pp.230-236, 2009 Feb.
  84. I. Takeuchi, K. Nomura, and T. Kanamori Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression. Neural Computation, vol.21, no.2, pp.533-559, 2009 Feb.
  85. M. Nakagawa, A. Oshiro, S. Karnan, H. Tagawa, A. Usunomiya, S. Nakamura, I. Takeuchi, K. Ohshima, and M. Seto Array comparative genomic hybridization analysis of PTCL-U reveals a distinct subgroup with genetic alterations similar to lymphoma-type adult T-cell leukemia/lymphoma. Clinical Cancer Research, vol.15, pp.30-38, 2009 Jan.
  86. I. Takeuchi, H. Tagawa, A. Tsujikawa, M. Nakagawa, M. Katayama, Y. Guo, and M. Seto The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis. Haematologica-The Hematology Journal, vol.94, pp.61-69, 2009 Jan.
  87. Y. Tsukamoto, T. Karnan, S. Uchida, T. Noguchi, N. Tung, M. Tanigawa, I. Takeuchi, K. Matsuura, N. Hijiya, C. Nakada, T. Kishida, H. Ito, K. Murakami, T. Fujioka, M. Seto, and M. Moriyama Genome-wide analysis of DNA copy number alterations and gene expression in gastric cancer. Journal of Pathology, vol.216, no.4, pp.471-82, 2008 Dec.
  88. T. Yoshimoto, K. Matsuura, S. Karnan, H. Tagawa, C. Nakada, M. Tanigawa, Y. Tsukamoto, T. Uchida, K. Kashima, S. Akizuki, I. Takeuchi, F. Sato, H. Mimata, M. Seto, and M. Moriyama High-resolution analysis of DNA copy number alterations and gene expression in renal clear cell carcinoma. Journal of Pathology, vol.213, pp.392-401, 2007 Dec.
  89. N. Fukuhara, T. Nakamura, M. Nakagawa, H. Tagawa, I. Takeuchi, Y. Yatabe Y. Morishima, S. Nakamura, and M. Seto, Chromosomal Imbalances are associated with outcome of helicobacter pylori eradication in t(11;18) (q21;q21) negative gastric mucosa-associated lymphoid tissue lymphomas. Genes Chromosomes and Cancer, vol.46, pp.784-790, 2007 Aug.
  90. I. Takeuchi, Q.V. Le, T.D Sears, and A.J Smola Nonparametric quantile estimation. Journal of Machine Learning Research, vol.7, pp.1231-1264, 2006 Dec.
  91. 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, pp.3605-3618, 2006 Aug.
  92. I. Takeuchi, Y. Bengio, and T. Kanamori, Robust regression with asymmetric heavy-tail noise distributions. Neural Computation, vol.14, pp.2469-2496, 2002 Oct.
  93. I. Takeuchi and T. Furuhashi Modeling for dynamic systems with fuzzy sequential knowledge. Studies in Fuzziness and Soft Computing, vol.59, pp.104-120, 2001.
  94. I. Takeuchi and T. Furuhashi Modeling of sensory/motor systems for autonomous agents. Journal of Artificial Life and Robotics, vol.4, pp.84-88, 2000.
  95. I. Takeuchi and T. Furuhashi Acquisition of manipulative grounded symbols for integration of symbolic processing and stimulus-reaction type parallel processing. The International Journal of the Robotics Society of Japan, vol.12, pp.271-287, 1997.

ジャーナル論文(日本語)

  1. I. Takeuchi Parametric programming for machine learning algorithm (in Japanese). Transactions of the Japan Society for Industrial and Applied Mathematics, vol.23, pp.517-536, 2013.
  2. K. Nishi and I. Takeuchi Casualty insurance pure premium estimation using two-stage regression tree (in Japanese). Transactions of the Japanese Society for Artificial Intelligence, vol.22, pp.183-190, 2007 Nov.
  3. I. Takeuchi, T. Furuhashi, Y. Hamada, and Y. Uchikawa Recollection of concepts by association of vague patterns (in Japanese). Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, vol.9, pp.505-511, 1997.

国際会議論文

  1. S. Iwazaki, T. Tanabe, M. Irie, S. Takeno, Y. Inatsu Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), to appear (Acceptance rate: 0.276).
  2. Y. Inatsu, S. Takeno, H. Hanada, K. Iwata, I. Takeuchi Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS2024), to appear (Acceptance rate: 0.276).
  3. R. Ozaki, K. Ishikawa, Y. Kanzaki, S. Takeno, I. Takeuchi and M. Karasuyama, Multi-objective Bayesian Optimization with Active Preference Learning. The 38th AAAI Conference on Artificial Intelligence (AAAI 2024), to appear (Acceptance rate: 0.23).
  4. S. Takeno, Y. Inatsu and M. Karasuyama, Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds. Proceedings of The 40th International Conference on Machine Learning (ICML 2023), PMLR 202:33490-33515, 2023 (Acceptance rate: 0.27).
  5. S. Takeno, M. Nomura and M. Karasuyama, Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. Proceedings of The 40th International Conference on Machine Learning (ICML 2023), PMLR 202:33516-33533, 2023 (Acceptance rate: 0.27).
  6. H. Ishibashi, M. Karasuyama, I. Takeuchi, and H. Hino, A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), PMLR 206:6463-6497, 2023 (Acceptance rate: 29%).
  7. S. Takeno, M. Nomura, and M. Karasuyama, Preferential Bayesian Optimization with Hallucination Believer. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022.
  8. Y. Inatsu, S. Takeno, M. Karasuyama, and I. Takeuchi, Bayesian Optimization for Distributionally Robust Chance-constrained Problem. Proceedings of International Conference on Machine Learning 2022 (ICML2022), 2022, July. (acceptance rate 0.22)
  9. S. Takeno, T. Tamura, K. Shitara. and M. Karasuyama, Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound. Proceedings of International Conference on Machine Learning 2022 (ICML2022), 2022, July. (acceptance rate 0.22)
  10. R. Koga, N. Hashimoto, T. Yokota, M. Nakaguro, K. Kohno, S. Nakamura, T. Takeuchi and H. Hontani Detection of DLBCL regions in H&E stained whole slide pathology images using Bayesian U-Net. Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 1179203 , 2021.
  11. R. Koga, N. Hashimoto, T. Yokota, M. Nakaguro, K. Kohno, S. Nakamura, I. Takeuchi and H. Hontani Stain transfer for automatic annotation of malignant lymphoma regions in H&E stained whole slide histopathology images. Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920R , 2021.
  12. V.N.L. Duy, I. Takeuchi Parametric Programming Approach for More Powerful and General Lasso Selective Inference. The 24th International Conference on Artifical Intelligence and Statistics (AISTATS2021), 2021 Apr. (acceptance rate 0.30)
  13. S. Iwazaki, Y. Inatsu, I. Takeuchi Mean-Variance Analysis in Bayesian Optimization under Uncertainty. The 24th International Conference on Artifical Intelligence and Statistics (AISTATS2021), 2021 Apr. (acceptance rate 0.30)
  14. K. Sugiyama, V.N.L. Duy, I. Taketuchi More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method. Proceedings of International Conference on Machine Learning 2021 (ICML2021), 2021, Jul. (acceptance rate 0.21)
  15. Y. Inatsu, S. Iwazaki, I. Taketuchi Active Learning for Distributionally Robust Level-Set Estimation. Proceedings of International Conference on Machine Learning 2021 (ICML2021), 2021 Jul. (acceptance rate 0.21)
  16. V.N.L. Duy, H. Toda, R. Sugiyama, I. Taketuchi Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming. Proceedings of 34th Conference on Neural Information Processing Systems (NeurIPS2020), 2020 Dec. (acceptance rate 0.20)
  17. S. Suzuki, S. Takeno, T. Tamura, K. Shitara. and M. Karasuyama, Multi-objective Bayesian Optimization using Pareto-frontier Entropy. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 2020 Jul. (acceptance rate 0.22)
  18. S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M. Shiga, I. Takeuchi and M. Karasuyama, Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), 2020 Jul. (acceptance rate 0.22)
  19. N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani and I. Takeuchi Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 Jun. (acceptance rate 0.22)
  20. K. Tanizaki, N. Hashimoto, Y. Inatsu, H. Hontani and I. Takeuchi Computing Valid P-Values for Image Segmentation by Selective Inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 Jun. (acceptance rate 0.22)
  21. E. Ndiaye and I. Takeuchi Computing Full Conformal Prediction Set with Approximate Homotopy. Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), pp.1386-1395, 2019 Dec. (acceptance rate 0.21)
  22. V.N.L. Duy, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, Y. Tabei, and I. Takeuchi Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2019), pp.548-551, 2019 Nov. (acceptance rate 0.20)
  23. T. Yoshida, I. Takeuchi, and M. Karasuyama, Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), pp.1026-1036, 2019 Aug. (acceptance rate 0.15)
  24. E. Ndiaye, T. Le, O. Fercoq, J. Salmon, and I. Takeuchi Safe Grid Search with Optimal Complexity. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), vol.97, pp.4771-4780, 2019 Jun. (acceptance rate 0.23)
  25. M. Yamada, D. Wu, Y.H.H Tsai, H. Ohta, R. Salakhutdinov, I. Takeuchi, and K. Fukumizu Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator. Proceedings of the 7th International Conference on Learning Representations (ICLR 2019), 2019 May. (acceptance rate 0.31)
  26. T. Yoshida, I. Takeuchi, and M. Karasuyama, Safe triplet screening for distance metric learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pp.2653-2662, 2018 Aug. (acceptance rate 0.18)
  27. T. Sakuma, K. Nishi, S.j. Yamazaki, K.D. Kimura, S. Matsumoto, K. Yoda, and I. Takeuchi Finding Discriminative Animal Behaviors from Sequential Bio-logging Trajectory Data. Proceedings of the 6th International Conference on Distributed, Ambient and Pervasive Interactions (DAPI 2018), vol.2, pp.125-138, 2018 Jul.
  28. M. Y. Yamada Umezu, K. Fukumizu, and I. Takeuchi Post Selection Inference with Kernels. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), vol.84, pp.152-160, 2018 Apr. (acceptance rate 0.33)
  29. M. Karasuyama, and H. Mamitsuka Factor Analysis on a Graph. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), vol.84, pp.1117-1126, 2018 Apr. (acceptance rate 0.33)
  30. H. Hanada, A. Shibagaki, J. Sakuma, and I. Takeuchi Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), pp.1314-1321, 2018 Feb. (acceptance rate 0.25)
  31. S. Suzumura, Y. Umezu, K. Tsuda, and I. Takeuchi Selective Inference for Sparse High-Order Interaction Models. Proceedings of the 34th International Conference on Machine Learning (ICML 2017), vol.70, pp.3338-3347, 2017 Aug. (acceptance rate 0.27)
  32. K. Kusano, I. Takeuchi, and J. Sakuma Privacy-preserving and optimal interval release for disease susceptibility. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security (ASIA CCS 2017), pp.532-545, 2017 Apr.
  33. T. Takada, H. Hanada, Y. Yamada, J. Sakuma, and I. Takeuchi Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization. Proceedings of the 8th Asian Conference on Machine Learning (ACML 2016), pp.126-141, 2016 Nov. (acceptance rate 0.24)
  34. K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, and I. Takeuchi Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining. Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), pp.1785-1794, 2016 Aug. (acceptance rate 0.18)
  35. A. Shibagaki, M. Karasuyama, K. Hatano, and I. Takeuchi Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), vol.48, pp.1577-1586, 2016 Jun. (acceptance rate 0.24)
  36. A. Shibagaki, Y. Suzuki, M. Karasuyama, and I. Takeuchi Regularization Path of Cross-Validation Error Lower Bounds. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NeurIPS 2015), pp.1675-1683, 2015 Dec. (acceptance rate 0.22)
  37. S. Okumura, Y. Suzuki, and I. Takeuchi Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems. Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015), pp.885-894, 2015 Aug. (acceptance rate 0.19)
  38. S. Suzumura, K. Ogawa, M. Sugiyama, and I. Takeuchi Outlier Path: A Homotopy Algorithm for Robust SVM. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), vol.32, no.2, pp.1098-1106, 2014 Jun. (acceptance rate 0.25)
  39. I. Takeuchi, T. Hongo, M. Sugiyama, and S. Nakajima Parametric Task Learning. Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NeurIPS 2013), pp.1358-1366, 2013 Dec. (acceptance rate 0.25)
  40. S. Nakajima, A. Takeda, S.D. Babacan, M. Sugiyama, and I. Takeuchi Global solver and its efficient approximation for variational bayesian low-rank subspace clustering. Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NeurIPS 2013), pp.1439-1447, 2013 Dec. (acceptance rate 0.25)
  41. K. Ogawa, Y. Suzuki, and I. Takeuchi Safe screening of non-support vectors in pathwise SVM computation. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), vol.28, no.3, pp.1382-1390, 2013 Jun. (acceptance rate 0.27)
  42. K. Ogawa, M. Imamura, I. Takeuchi, and M. Sugiyama Infinitesimal annealing for training semi-supervised support vector machines. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), vol.28, no.3, pp.897-905, 2013 Jun. (acceptance rate 0.27)
  43. M. Sugiyama, T. Kanamori, T. Suzuki, M. Plessis, S. Liu, and I. Takeuchi Density-Difference Estimation. Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NeurIPS 2012), pp.683-691, 2012 Dec. (acceptance rate 0.25)
  44. I. Takeuchi and M. Sugiyama Target neighbor consistent feature weighting for nearest neighbor classification. Proceedings of the 25th Annual Conference on Neural Information Processing Systems (NeurIPS 2011), pp.576-584, 2011 Dec. (acceptance rate 0.22)
  45. M. Karasuyama, N. Harada, M. Sugiyama, and I. Takeuchi Multi-parametric solution-path algorithm for instance-weighted support vector machines. Proceedings of 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), 2011 Sep.
  46. M. Karasuyama, and I. Takeuchi Suboptimal solution path algorithm for support vector machine. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp.473-480, 2011 Jun. (acceptance rate 0.26)
  47. M. Karasuyama, and I. Takeuchi Nonlinear regularization path for the support vector machines with the quadratic loss function. Proceedings of 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp.3099-3106, 2010 Jul.
  48. Y. Ishikawa and I. Takeuchi Detecting differentially aberrant genomic regions in multi-sample array CGH experiments using nearest-neighbor multivariate test. Proceedings of 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp.1547-1554, 2010 Jul.
  49. M. Sugiyama, I. Takeuchi, T. Suzuki, T. Kanamori, H. Hachiya, and D. Okanohara Conditional density estimation via least-squares density ratio estimation. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), vol.9, pp.781-788, 2010 May.
  50. M. Karasuyama and I. Takeuchi Multiple incremental decremental learning of support vector machine. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NeurIPS 2009), pp.907-915, 2009 Dec. (acceptance rate 0.24)
  51. Y. Ishikawa, I. Takeuchi, and R. Nakano Variational Bayes from the Primitive Initial Point for Gaussian Mixture Estimation. Proceedings of the 16th International Conference on Neural Information Processing (ICONIP 2009), LNCS 5863, pp 159-166, 2009 Dec.
  52. N. Harada, Y. Ishikawa, I. Takeuchi, and R. Nakano A Bayesian Graph Clustering Approach Using Degree Distribution Prior. Proceedings of the 16th International Conference on Neural Information Processing (ICONIP 2009), LNCS 5863, pp.167-174, 2009 Dec.
  53. I. Takeuchi, M. Nakagawa, and M. Seto Metric Learning for DNA microarray data analysis. Proceedings of International Workshop on Statistical-Mechanical Informatics 2009 (IW-SMI 2009), vol.197, no.1, 012008, 2009 Sep.
  54. I. Takeuchi Statistical significance analysis of gene groups using nearest-neighbor classification performance. Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2008), pp.1989-1994, 2008 Sep.
  55. H. Moriguchi and I. Takeuchi Adaptive kernel quantile regression for anomaly detection of time series. Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2008), pp.1831-1836, 2008 Sep.
  56. M. Karasuyama, I. Takeuchi, and R. Nakano Reducing SVR support vectors by using backward deletion. Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2008), LNAI 5179, pp.76-83, 2008 Sep.
  57. I. Takeuchi, K. Nomura, and T. Kanamori The entire solution path of kernel-based nonparametric conditional quantile estimator. Proceedings of 2006 International Joint Conference on Neural Networks (IJCNN 2006), pp.153-158, 2006 Jul.
  58. T. Kanamori and I. Takeuchi Estimator for conditional expectations under asymmetric and heterocedastic error distribution. Proceedings of the International Symposium on the Art of Statistical Metaware, pp.312-313, 2005 Mar.
  59. I. Takeuchi and T. Furuhashi Non-crossing quantile regression by SVM. Proceedings of 2004 International Joint Conference on Neural Networks (IJCNN 2004), pp.401-406, 2004 Jul.
  60. I. Takeuchi, N. Yamanaka, and T. Furuhashi Robust regression under asymmetric or/and non-constant variance error by simultaneously training conditional quantiles. Proceedings of 2003 International Joint Conference on Neural Networks (IJCNN 2003), pp.1729-1734, 2003 Jul.
  61. Y. Bengio, I. Takeuchi, and T. Kanamori The challenge of non-linear regression on large datasets with asymmetric heavy tail. Proceedings of 2002 Joint Statistical Meetings (JSM 2002), pp.193-205, 2002 Aug.
  62. N. Chapados, Y. Bengio, P. Vincent, J. Ghosn, C. Dugas, I. Takeuchi, and L. Meng Estimating car insurance premia: a case study in high-dimensional data inference. Proceedings of the 15th Annual Conference on Neural Information Processing Systems (NeurIPS 2001), pp.1369-1376, 2001 Dec.
  63. I. Takeuchi and T. Furuhashi A study on fuzzy modeling for dynamic characteristic. Proceedings of 1999 IEEE International Conference on Systems, Man and Cybernetics, vol.3, pp.34-39, 1999 Oct.
  64. I. Takeuchi and T. Furuhashi A proposal of fuzzy modeling for dynamic characteristics in state-space description. Proceedings of 1999 IEEE International Conference on Fuzzy Systems, vol.2, pp.807-812, 1999 Aug.
  65. I. Takeuchi and T. Furuhashi Integration of symbolic processing and parallel distributed processing by acquisition of manipulative grounded symbol. Proceedings of 1998 World Automation Congress, pp.126.1-126.6, 1998 May.
  66. I. Takeuchi and T. Furuhashi Self-organization of grounded symbols for fusions of symbolic processing and parallel distribted processing. Proceedings of 1998 IEEE International Conference on Fuzzy Systems, pp.715-720, 1998 May.
  67. I. Takeuchi and T. Furuhashi A proposal of architecture for intelligent systems with manipulative grounded symbol. Proceedings of the 2nd International Conference on Knowledge-Based Intelligent Electronic Systems (KES 1998), pp.325-330, 1998 Apr.
  68. I. Takeuchi and T. Furuhashi A proposal of self-organizing network for acquisition of vague concept. Proceedings of 1996 Asian Fuzzy System Symposium (AFSS 1996), pp.85-90, 1996 Dec.
  69. I. Takeuchi and T. Furuhashi A self-organizing network for acquisition of vague concept. Proceedings of 1996 Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 1996), pp.473-480, 1996 Nov.
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