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 (IEEE Congress on Evolutionary Computation, 2011)

A new real-coded genetic algorithm for implicit constrained black-box function optimization. - Kento Uemura; Naotoshi Nakashima; Yuichi Nagata; Isao Ono (IEEE Congress on Evolutionary Computation, 2013)

AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation. - Kento Uemura; Isao Ono (J. Adv. Comput. Intell. Intell. Informatics, 2016)

Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. - Kento Uemura; Shohei Shimizu (ICASSP, 2020)

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. - Kentaro Kanamori; Takuya Takagi; Ken Kobayashi; Yuichi Ike; Kento Uemura; Hiroki Arimura (AAAI, 2021)

A Multivariate Causal Discovery based on Post-Nonlinear Model. - Kento Uemura; Takuya Takagi; Kambayashi Takayuki; Hiroyuki Yoshida; Shohei Shimizu (CLeaR, 2022)

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 (PRIMA, 2022)

High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition. - Shuyu Dong; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi; Michèle Sebag (CoRR, 2022)

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 (WSC, 2023)

DCDILP: a distributed learning method for large-scale causal structure learning. - Shuyu Dong; Michèle Sebag; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi (CoRR, 2024)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning. - Shuyu Dong; Michèle Sebag; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi (AAAI, 2025)