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Kentaro Minami
Kentaro Minami
PayPay Corporation
Verified email at ktrmnm.jp
Title
Cited by
Cited by
Year
Smoothness and stability in gans
C Chu, K Minami, K Fukumizu
arXiv preprint arXiv:2002.04185, 2020
652020
Differential privacy without sensitivity
K Minami, HI Arai, I Sato, H Nakagawa
Advances in Neural Information Processing Systems 29, 2016
592016
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
262021
Trader-company method: a metaheuristic for interpretable stock price prediction
K Ito, K Minami, K Imajo, K Nakagawa
arXiv preprint arXiv:2012.10215, 2020
182020
The equivalence between Stein variational gradient descent and black-box variational inference
C Chu, K Minami, K Fukumizu
arXiv preprint arXiv:2004.01822, 2020
122020
Degrees of freedom in submodular regularization: A computational perspective of Stein’s unbiased risk estimate
K Minami
Journal of Multivariate Analysis 175, 104546, 2020
122020
Estimating piecewise monotone signals
K Minami
Electronic Journal of Statistics 14 (1), 1508-1576, 2020
112020
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
92021
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
32022
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
22022
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
12023
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
12022
Contrastive representation learning with trainable augmentation channel
M Koyama, K Minami, T Miyato, Y Gal
arXiv preprint arXiv:2111.07679, 2021
12021
What Data Augmentation Do We Need for Deep-Learning-Based Finance?
L Ziyin, K Minami, K Imajo
arXiv preprint arXiv:2106.04114, 2021
12021
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
2023
Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
R Assabumrungrat, K Minami, M Hirano
arXiv preprint arXiv:2311.07231, 2023
2023
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
2023
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
Differential Privacy and Pseudo-Bayesian Posterior
K Minami, H Arai, I Sato, H Nakagawa
IEICE Technical Report; IEICE Tech. Rep. 115 (112), 39-46, 2015
2015
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Articles 1–20