| Deep portfolio optimization via distributional prediction of residual factors K Imajo, K Minami, K Ito, K Nakagawa Proceedings of the AAAI conference on artificial intelligence 35 (1), 213-222, 2021 | 29 | 2021 |
| Calibrating research in program synthesis using 72,000 hours of programmer time T Akiba, K Imajo, H Iwami, Y Iwata, T Kataoka, N Takahashi, M Moskal, ... MSR, Redmond, WA, USA, Tech. Rep, 2013 | 21 | 2013 |
| Trader-company method: a metaheuristic for interpretable stock price prediction K Ito, K Minami, K Imajo, K Nakagawa arXiv preprint arXiv:2012.10215, 2020 | 19 | 2020 |
| No-transaction band network: A neural network architecture for efficient deep hedging S Imaki, K Imajo, K Ito, K Minami, K Nakagawa arXiv preprint arXiv:2103.01775, 2021 | 10 | 2021 |
| Calibrating research in program synthesis using 72,000 hours of programmer time. MSR, Redmond, WA T Akiba, K Imajo, H Iwami, Y Iwata, T Kataoka, N Takahashi, M Moskal, ... USA, Tech. Rep, 2013 | 6 | 2013 |
| Uncertainty aware trader-company method: Interpretable stock price prediction capturing uncertainty Y Fujimoto, K Nakagawa, K Imajo, K Minami 2022 IEEE International Conference on Big Data (Big Data), 1238-1245, 2022 | 4 | 2022 |
| Fast gaussian filtering algorithm using splines K Imajo Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 3 | 2012 |
| Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling M Hirano, K Minami, K Imajo Proceedings of the Fourth ACM International Conference on AI in Finance, 19-26, 2023 | 2 | 2023 |
| Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction L Ziyin, K Minami, K Imajo Proceedings of the Third ACM International Conference on AI in Finance, 273-281, 2022 | 2 | 2022 |
| Semi-supervised ligand finding using formal concept analysis M Sugiyama, K Imajo, K Otaki, A Yamamoto Information and Media Technologies 7 (3), 928-937, 2012 | 2 | 2012 |
| Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training M Hirano, K Imajo arXiv preprint arXiv:2404.10555, 2024 | 1 | 2024 |
| Efficient Learning of Nested Deep Hedging using Multiple Options M Hirano, K Imajo, K Minami, T Shimada 2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI …, 2023 | 1 | 2023 |
| What Data Augmentation Do We Need for Deep-Learning-Based Finance? L Ziyin, K Minami, K Imajo arXiv preprint arXiv:2106.04114, 2021 | 1 | 2021 |
| Contest environment using wireless networks: a case study from Japan K Imajo Olympiads in Informatics 5, 26-31, 2011 | 1 | 2011 |
| Power laws and symmetries in a minimal model of financial market economy L Ziyin, K Ito, K Imajo, K Minami Physical Review Research 4 (3), 033077, 2022 | | 2022 |
| Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis M Sugiyama, K Imajo, K Otaki, A Yamamoto Latest Advances In Inductive Logic Programming, 53-60, 2015 | | 2015 |
| Gauging Research in Program Synthesis with a Massive Open Online Contest T Akiba, K Imajo, H Iwami, Y Iwata, T Kataoka, N Takahashi, M Moskal, ... | | |
Kentaro MinamiPayPay Corporation在 ktrmnm.jp 的电子邮件经过验证
