Mathematical Informatics Lab. 6


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山西 健司
Kenji Yamanishi

鈴木 大慈
Taiji Suzuki

Associate Professor
久野 遼平
Ryohei Hisano

Information-theoretic learning theory/Statistical learning theory
"What are the possibility and limitation of machine learning?" We take information-theoretic and statistical approaches to answer this question. As for information-theoretic learning theory, we study a unifying methodology for model selection, representation learning, change detection, high-dimensional sparse learning, etc. on the basis of the minimum description length principle. As for statistical learning, we study new algorithm designs and theoretical analysis of deep learning and kernel methods on the basis of statistical theory. We also develop new optimization methods to run the machine learning algorithms efficiently on the big data.

Data Science Foundation
We study methodologies for knowledge discovery from big data (anomaly detection, network mining, embedding, etc.) Specifically we are interested in discovering deep knowledge from latent spaces. We aim at building a new field called "Symptomatics", in which we detect signs of latent changes in future from data streams.

Data Science Applications
We study effective data science methodologies by applying machine learning and data mining technologies to real complex data. The applications include economics, financial data analysis, medical data analysis, marketing, SNS data analysis, failure detection, spatial data mining, security, etc. We often collaborate with industrial companies to solve real data science problems.