ジャーナル論文 / Journal
  1. Yoshida, T., Hanada, H., Nakagawa, K., Taji, K., Tsuda, K., and Takeuchi, I., "Efficient model selection for predictive pattern mining model by safe pattern pruning", Patterns 4(12), 100890, (2023).
  2. Yamaguchi, Y., Atsumi, T., Kanamori, K., Tanibata, N., Takeda, H., Nakayama, M., Karasuyama, M., and Takeuchi, I., "Drawing a materials map with an autoencoder for lithium ionic conductors", Sci. Rep. 13(1), 16799, (2023).
  3. Takagi, Y., Hashimoto, N., Masuda, H., Miyoshi, H., Ohshima, K., Hontani, H., and Takeuchi, I., "Transformer-based personalized attention mechanism for medical images with clinical records", Journal of Pathology Informatics 14, (2023).
  4. Nagaishi, M., Miyoshi, H., Kugler, M., Sato, K., Kohno, K., Takeuchi, M., Yamada, K., Furuta, T., Hashimoto, N., Takeuchi, I., Hontani, H., and Ohshima, K., "The Detection of Neoplastic Cells Using Objective Cytomorphologic Parameters in Malignant Lymphoma", Lab. Invest. 104(3), 100302, (2023).
  5. Hiroyuki, H., Hashimoto, N., Taji, K., and Takeuchi, I., "Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications", Neural Comput. 35(12), 1970–2005, (2023).
  6. Hashimoto, N., Takagi, Y., Masuda, H., Miyoshi, H., Kohno, K., Nagaishi, M., Sato, K., Takeuchi , M., Furuta, T., Kawamoto, K., Yamada, K., Moritsubo, M., Inoue, K., Shimasaki, Y., Ogura, Y., Imamoto, T., Mishina, T., Tanaka, K., Kawaguchi, Y., Nakamura, S., Ohshima, K., Hontani, H., and Takeuchi, I., "Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning", Med. Image Anal. 85, (2023).
  7. Hashimoto, N., Hanada, H., Miyoshi, H., Nagaishi, M., Sato, K., Hontani, H., Ohshima, K., and Takeuchi, I., "Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma", Journal of Pathology Informatics, 100359, (2023).
  8. Goto, K., Tamehiro, N., Yoshida, T., Hanada, H., Sakuma, T., Adachi, R., Kondo, K., and Takeuchi, I., "Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences", J. Biol. Chem. 299(6), 104733, (2023).
  9. Fuse, Y., Takeuchi, K., Hashimoto, N., Nagata, Y., Takagi, Y., Nagatani, T., Takeuchi, I., and Saito, R., "Deep learning based identification of pituitary adenoma on surgical endoscopic images: a pilot study", Neurosurg. Rev. 46(1), 291, (2023).
  10. Suzuki, K., Tange, M., Yamagishi, R., Hanada, H., Mukai, S., Sato, T., Tanaka, T., Akashi, T., Kadomatsu, K., Maeda, T., Miida, T., Takeuchi, I., Murakami, H., Sekido, Y., and Murakami-Tonami, Y., "SMG6 regulates DNA damage and cell survival in Hippo pathway kinase LATS2-inactivated malignant mesothelioma", Cell Death Discovery 8(1), (2022).
  11. Hashimoto, N., Ko, K., Yokota, T., Kohno, K., Nakaguro, M., Nakamura, S., Takeuchi, I., and Hontani, H., "Subtype classification of malignant lymphoma using immunohistochemical staining pattern", International Journal of Computer Assisted Radiology and Surgery, (2022).
国際会議 / Proceedings
  1. Ozaki, R., Ishikawa, K., Kanzaki, Y., Suzuki, S., Takeno, S., Takeuchi, I., and Karasuyama, M., "Multi-objective Bayesian Optimization with Active Preference Learning", Proceedings of the 38th AAAI Conference on Artificial Intelligence, (2024).
  2. Iwazaki, S., Tanabe, T., Irie, M., Takeno, S., and Inatsu, Y., "Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum", Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, (2024).
  3. Inatsu, Y., Takeno, S., Hanada, H., Iwata, K., and Takeuchi, I., "Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty", Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, (2024).
  4. Takeno, S., Nomura, M., and Karasuyama, M., "Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes", Proceedings of the 40th International Conference on Machine Learning (ICML) 202, 33516–33533, (2023).
  5. Takeno, S., Inatsu, Y., and Karasuyama, M., "Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds", Proceedings of the 40th International Conference on Machine Learning (ICML) 202, 33490–33515, (2023).
  6. Iwazaki, S., Takeno, S., Tanabe, T., and Mitsuru, I., "Failure-Aware Gaussian Process Optimization with Regret Bounds", Proceedings of Advances in Neural Information Processing Systems 36 , (2023).
  7. Das, D., Vo, D. N., and Takeuchi, I., "Fast and More Powerful Selective Inference for Sparse High-order Interaction Model", Proceedings of AAAI Conference on Artificial Intelligence (AAAI2022), (2022).
  8. Vo, D. N., and Takeuchi, I., "Parametric Programming Approach for More Powerful and General Lasso Selective Inference", Proceedings of The 24th International Conference on Artifical Intelligence and Statistics (AISTATS2021), (2021).
  9. Sugiyama, K., Vo, D. N., and Takeuchi, I., "More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method", Proceedings of International Conference on Machine Learning 2021 (ICML2021), (2021).
  10. Iwazaki, S., Inatsu, Y., and Takeuchi, I., "Mean-Variance Analysis in Bayesian Optimization under Uncertainty", Proceedings of The 24th International Conference on Artifical Intelligence and Statistics (AISTATS2021), (2021).
  11. Inatsu, Y., Iwazaki, S., and Takeuchi, I., "Active Learning for Distributionally Robust Level-Set Estimation", Proceedings of International Conference on Machine Learning 2021 (ICML2021), (2021).