Smoothness and stability in gans C Chu, K Minami, K Fukumizu arXiv preprint arXiv:2002.04185, 2020 | 78 | 2020 |
Differential privacy without sensitivity K Minami, HI Arai, I Sato, H Nakagawa Advances in Neural Information Processing Systems 29, 2016 | 75 | 2016 |
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 | 42 | 2021 |
Trader-company method: a metaheuristic for interpretable stock price prediction K Ito, K Minami, K Imajo, K Nakagawa arXiv preprint arXiv:2012.10215, 2020 | 24 | 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 | 17 | 2021 |
The equivalence between Stein variational gradient descent and black-box variational inference C Chu, K Minami, K Fukumizu arXiv preprint arXiv:2004.01822, 2020 | 15 | 2020 |
Estimating piecewise monotone signals K Minami Electronic Journal of Statistics 14 (1), 1508-1576, 2020 | 11 | 2020 |
Degrees of freedom in submodular regularization: A computational perspective of Stein’s unbiased risk estimate K Minami Journal of Multivariate Analysis 175, 104546, 2020 | 10 | 2020 |
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 | 6 | 2022 |
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 | 4 | 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 | 3 | 2022 |
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 | 2 | 2023 |
Contrastive representation learning with trainable augmentation channel M Koyama, K Minami, T Miyato, Y Gal arXiv preprint arXiv:2111.07679, 2021 | 2 | 2021 |
A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets–A New Microfoundations of GARCH Model K Nakagawa, M Hirano, K Minami, T Mizuta International Conference on Principles and Practice of Multi-Agent Systems …, 2024 | 1 | 2024 |
Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics M Kato, M Imaizumi, K Minami International Conference on Artificial Intelligence and Statistics, 5271-5298, 2023 | 1 | 2023 |
Unified perspective on probability divergence via maximum likelihood density ratio estimation: Bridging KL-divergence and integral probability metrics M Kato, M Imaizumi, K Minami arXiv preprint arXiv:2201.13127, 2022 | 1 | 2022 |
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 |
Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study R Assabumrungrat, K Minami, M Hirano 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI …, 2024 | | 2024 |
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 |
Degrees of freedom in submodular regularization K Minami, F Komaki IEICE Technical Report; IEICE Tech. Rep. 116 (500), 17-24, 2017 | | 2017 |