Takashi Nicholas Maeda

18 papers A* 1A 2Journal 12Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. AAAI Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2026	J	`jnl`	I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. CoRR Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2025	J	`jnl`	Causal Additive Models with Unobserved Causal Paths and Backdoor Paths. CoRR Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu
2025	J	`jnl`	Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data. CoRR Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui
2024	J	`jnl`	Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2023	J	`jnl`	Python package for causal discovery based on LiNGAM. J. Mach. Learn. Res. Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
2022	J	`jnl`	Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. Int. J. Data Sci. Anal. Takashi Nicholas Maeda, Shohei Shimizu
2021	A	`conf`	Causal additive models with unobserved variables. UAI Takashi Nicholas Maeda, Shohei Shimizu
2021	J	`jnl`	Discovery of Causal Additive Models in the Presence of Unobserved Variables. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2020	J	`jnl`	Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. CoRR Takashi Nicholas Maeda, Shohei Shimizu
2020	A	`conf`	RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. AISTATS Takashi Nicholas Maeda, Shohei Shimizu
2019	J	`jnl`	Comparative Examination of Network Clustering Methods for Extracting Community Structures of a City From Public Transportion Smart Card Data. IEEE Access Takashi Nicholas Maeda, Junichiro Mori, Izumi Hayashi, Tetsuo Sakimoto, Ichiro Sakata
2019	J	`jnl`	Detecting and understanding urban changes through decomposing the numbers of visitors' arrivals using human mobility data. J. Big Data Takashi Nicholas Maeda, Narushige Shiode, Chen Zhong, Junichiro Mori, Tetsuo Sakimoto
2018	J	`jnl`	Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS Int. J. Geo Inf. Takashi Nicholas Maeda, Mitsuo Yoshida, Fujio Toriumi, Hirotada Ohashi
2018	J	`jnl`	Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation. ISPRS Int. J. Geo Inf. Takashi Nicholas Maeda, Junichiro Mori, Masanao Ochi, Tetsuo Sakimoto, Ichiro Sakata
2017	—	`conf`	Next place prediction in unfamiliar places considering contextual factors. SIGSPATIAL/GIS Takashi Nicholas Maeda, Kota Tsubouchi, Fujio Toriumi
2016	—	`conf`	Analysis of smart card data for understanding spatial changes in consumption-oriented human flows. UrbanGIS@SIGSPATIAL Takashi Nicholas Maeda, Junichiro Mori, Fujio Toriumi, Hirotada Ohashi
2016	—	`conf`	Decision Tree Analysis of Tourists' Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. Urb-IoT Takashi Nicholas Maeda, Mitsuo Yoshida, Fujio Toriumi, Hirotada Ohashi