| 2025 | AAAI | DCILP: A Distributed Approach for Large-Scale Causal Structure Learning. | Shuyu Dong, Michle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi |
| 2023 | WSC | An Iterative Analysis Method Using Causal Discovery Algorithms to Enhance ABM as a Policy Tool. | Shuang Chang, Takashi Kato, Yusuke Koyanagi, Kento Uemura, Koji Maruhashi |
| 2022 | PRIMA | Incorporating AI Methods in Micro-dynamic Analysis to Support Group-Specific Policy-Making. | Shuang Chang, Tatsuya Asai, Yusuke Koyanagi, Kento Uemura, Koji Maruhashi, Kotaro Ohori |
| 2021 | AAAI | Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. | Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura |
| 2020 | ICASSP | Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. | Kento Uemura, Shohei Shimizu |
| 2013 | CEC | A new real-coded genetic algorithm for implicit constrained black-box function optimization. | Kento Uemura, Naotoshi Nakashima, Yuichi Nagata, Isao Ono |
| 2011 | CEC | A new framework taking account of multi-funnel functions for Real-coded Genetic Algorithms. | Kento Uemura, Shun-ichi Kinoshita, Yuichi Nagata, Shigenobu Kobayashi, Isao Ono |