Analysis of smart card data for understanding spatial changes in consumption-oriented human flows. - Takashi Nicholas Maeda; Junichiro Mori; Fujio Toriumi; Hirotada Ohashi (UrbanGIS@SIGSPATIAL, 2016)

Decision Tree Analysis of Tourists' Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. - Takashi Nicholas Maeda; Mitsuo Yoshida 001; Fujio Toriumi; Hirotada Ohashi (Urb-IoT, 2016)

Next place prediction in unfamiliar places considering contextual factors. - Takashi Nicholas Maeda; Kota Tsubouchi; Fujio Toriumi (SIGSPATIAL/GIS, 2017)

Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation. - Takashi Nicholas Maeda; Junichiro Mori; Masanao Ochi; Tetsuo Sakimoto; Ichiro Sakata (ISPRS Int. J. Geo Inf., 2018)

Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. - Takashi Nicholas Maeda; Mitsuo Yoshida 001; Fujio Toriumi; Hirotada Ohashi (ISPRS Int. J. Geo Inf., 2018)

Comparative Examination of Network Clustering Methods for Extracting Community Structures of a City From Public Transportion Smart Card Data. - Takashi Nicholas Maeda; Junichiro Mori; Izumi Hayashi; Tetsuo Sakimoto; Ichiro Sakata (IEEE Access, 2019)

Detecting and understanding urban changes through decomposing the numbers of visitors' arrivals using human mobility data. - Takashi Nicholas Maeda; Narushige Shiode; Chen Zhong; Junichiro Mori; Tetsuo Sakimoto (J. Big Data, 2019)

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (AISTATS, 2020)

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2020)

Causal additive models with unobserved variables. - Takashi Nicholas Maeda; Shohei Shimizu (UAI, 2021)

Discovery of Causal Additive Models in the Presence of Unobserved Variables. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2021)

Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (Int. J. Data Sci. Anal., 2022)

Python package for causal discovery based on LiNGAM. - Takashi Ikeuchi; Mayumi Ide; Yan Zeng; Takashi Nicholas Maeda; Shohei Shimizu (J. Mach. Learn. Res., 2023)

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2024)

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths. - Thong Pham; Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2025)

Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data. - Takashi Nicholas Maeda; Shohei Shimizu; Hidetoshi Matsui (CoRR, 2025)