| Extraction of tourist destinations and comparative analysis of preferences between foreign tourists and domestic tourists on the basis of geotagged social media data TN Maeda, M Yoshida, F Toriumi, H Ohashi ISPRS International Journal of Geo-Information 7 (3), 99, 2018 | 58 | 2018 |
| RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders TN Maeda, S Shimizu International Conference on Artificial Intelligence and Statistics, 735-745, 2020 | 56 | 2020 |
| Python package for causal discovery based on LiNGAM T Ikeuchi, M Ide, Y Zeng, TN Maeda, S Shimizu Journal of Machine Learning Research 24 (14), 1-8, 2023 | 46 | 2023 |
| Causal additive models with unobserved variables TN Maeda, S Shimizu Uncertainty in Artificial Intelligence, 97-106, 2021 | 39 | 2021 |
| Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data TN Maeda, N Shiode, C Zhong, J Mori, T Sakimoto Journal of Big Data 6 (1), 4, 2019 | 27 | 2019 |
| The economic value of urban landscapes in a suburban city of Tokyo, Japan: A semantic segmentation approach using Google Street View images M Suzuki, J Mori, TN Maeda, J Ikeda Journal of Asian Architecture and Building Engineering 22 (3), 1110-1125, 2023 | 24 | 2023 |
| Comparative examination of network clustering methods for extracting community structures of a city from public transportation smart card data TN Maeda, J Mori, I Hayashi, T Sakimoto, I Sakata IEEE Access 7, 53377-53391, 2019 | 22 | 2019 |
| Decision tree analysis of tourists' preferences regarding tourist attractions using geotag data from social media TN Maeda, M Yoshida, F Toriumi, H Ohashi Proceedings of the Second International Conference on IoT in Urban Space, 61-64, 2016 | 22 | 2016 |
| Next place prediction in unfamiliar places considering contextual factors TN Maeda, K Tsubouch, F Toriumi The 25th ACM SIGSPATIAL International Conference on Advances in Geographic …, 2017 | 12 | 2017 |
| Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders TN Maeda, S Shimizu International Journal of Data Science and Analytics 13 (2), 77-89, 2022 | 7 | 2022 |
| 前田高志ニコラス, 三内顕義, 清水昌平, 星野利彦: EBPM と統計的因果探索・数理モデルの利活用 高山正行， 小柴 研究・イノベーション学会 第 36 回年次学術大会 (予稿集)., 公演番号 2G02, 2021 | 6 | 2021 |
| 前田高志ニコラス, 三内顕義, 清水昌平, 星野利彦: 博士課程進学率に関する因果モデルの構築: 統計的因果探索アルゴリズム “LiNGAM” による試行的分析 高山正行， 小柴 Jxiv preprint, 2022 | 5 | 2022 |
| Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders TN Maeda, S Shimizu arXiv preprint arXiv:2001.04197, 2020 | 5 | 2020 |
| Measurement of opportunity cost of travel time for predicting future residential mobility based on the smart card data of public transportation TN Maeda, J Mori, M Ochi, T Sakimoto, I Sakata ISPRS International Journal of Geo-Information 7 (11), 416, 2018 | 5 | 2018 |
| I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders TN Maeda Behaviormetrika 49 (2), 329-341, 2022 | 4 | 2022 |
| Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data TN Maeda, S Shimizu Behaviormetrika 52 (2), 529-547, 2025 | 3 | 2025 |
| 統計的因果探索アルゴリズム “LiNGAM” を用いた若手研究者支援政策に関する研究 高山正行， 小柴， 前田高志， 三内顕義， 清水昌平， 星野利彦 研究・イノベーション学会, 2021 | 3 | 2021 |
| Analysis of smart card data for understanding spatial changes in consumption-oriented human flows TN Maeda, J Mori, F Toriumi, H Ohashi Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban …, 2016 | 3 | 2016 |
| Causal additive models with unobserved causal paths and backdoor paths T Pham, TN Maeda, S Shimizu arXiv preprint arXiv:2502.07646, 2025 | 2 | 2025 |
| Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity TN Maeda, Y Zeng, S Shimizu Dependent data in social sciences research: Forms, issues, and methods of …, 2024 | 2 | 2024 |
SHIMIZU ShoheiThe University of Osaka (Osaka University), Shiga University & RIKENVerified email at ds.sanken.osaka-u.ac.jp
