Challenging the Limits of Information Extraction from Data
Takashi Isozaki
We are now using more data than ever before, and this usage is sure to go on increasing in the future. Many examples of mass-data can be listed, such as data related to geoenvironmental assessments, gene expressions, and buying history. It is easy to imagine that success in utilizing such data would have a great influence on the future of humanity. However, humankind does not currently have the ability to fully extract information from data, which I believe is a major problem. I have addressed this problem by treating data that have many variables dependent on each other using probabilistic graphical models and methodologies of physics.
Worldviews
CALC
A big Data Analysis Tool
Keywords
Causal information analysis
Data analysis
Open systems-data analytics
Statistical inference
Selected Publications
Takashi Isozaki and Manabu Kuroki,
Learning Causal Graphs with Latent Confounders in Weak Faithfulness ViolationsNew Generation Computing, | Vol.35, , pages 29-45, , 2017
Masatoshi Funabashi, Peter Hanappe, Takashi Isozaki, AnneMarie Maes, Takahiro Sasaki, Luc Steels and Kaoru Yoshida,
Foundation of CS-DC e-Laboratory: Open Systems Exploration for Ecosystems Leveraging,First Complex Systems Digital Campus World E-Conference 2015, | pages 351-374, , 2017
Takashi Isozaki,
A Robust Causal Discovery Algorithm against Faithfulness ViolationTrans. of the Japanese Society for Artificial Intelligence, | Vol.29, , pages 137-147, , 2014
News & Articles
[Article]A new article by Takashi Isozaki et al. has been published.
Practically effective adjustment variable selection in causal inference
'WORLD' by Researcher Takashi Isozaki is now available.
Challenging the Limits of Information Extraction from Data
[Article]A paper by Isozaki et al has been presented at AAAI 2024's GCLR Workshop
Adjustment Variable Selection for Intervention Estimation Robust to Data Size
