Kentaro Minami

According to our database, Kentaro Minami authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study.

Rawin Assabumrungrat, Kentaro Minami, Masanori Hirano

CoRR, 2023

Efficient Learning of Nested Deep Hedging using Multiple Options.

Masanori Hirano, Kentaro Imajo, Kentaro Minami, Takuya Shimada

Proceedings of the 14th IIAI International Congress on Advanced Applied Informatics, 2023

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling.

Masanori Hirano, Kentaro Minami, Kentaro Imajo

Proceedings of the 4th ACM International Conference on AI in Finance, 2023

Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics.

Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics.

Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

CoRR, 2022

Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction.

Ziyin Liu, Kentaro Minami, Kentaro Imajo

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty.

Yugo Fujimoto, Kei Nakagawa, Kentaro Imajo, Kentaro Minami

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Contrastive Representation Learning with Trainable Augmentation Channel.

Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal

CoRR, 2021

What Data Augmentation Do We Need for Deep-Learning-Based Finance?

Ziyin Liu, Kentaro Minami, Kentaro Imajo

CoRR, 2021

Trader-Company Method: A Metaheuristics for Interpretable Stock Price Prediction.

Katsuya Ito, Kentaro Minami, Kentaro Imajo, Kei Nakagawa

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Deep Portfolio Optimization via Distributional Prediction of Residual Factors.

Kentaro Imajo, Kentaro Minami, Katsuya Ito, Kei Nakagawa

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Degrees of freedom in submodular regularization: A computational perspective of Stein's unbiased risk estimate.

Kentaro Minami

J. Multivar. Anal., 2020

The equivalence between Stein variational gradient descent and black-box variational inference.

Casey Chu, Kentaro Minami, Kenji Fukumizu

CoRR, 2020

Smoothness and Stability in GANs.

Casey Chu, Kentaro Minami, Kenji Fukumizu

Proceedings of the 8th International Conference on Learning Representations, 2020

2016

Differential Privacy without Sensitivity.

Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016