Motoki Shiga

According to our database, Motoki Shiga authored at least 18 papers between 2006 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy.

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

Neural Comput., 2022

2020

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling.

Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, Motoki Shiga, Masayuki Karasuyama

CoRR, 2020

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization.

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Multi-fidelity Bayesian Optimization with Max-value Entropy Search.

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama

CoRR, 2019

2015

Non-Negative Matrix Factorization with Auxiliary Information on Overlapping Groups.

Motoki Shiga, Hiroshi Mamitsuka

IEEE Trans. Knowl. Data Eng., 2015

Direct conditional probability density estimation with sparse feature selection.

Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama

Mach. Learn., 2015

2014

Detecting Differentially Coexpressed Genesfrom Labeled Expression Data: A Brief Review.

Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka

IEEE ACM Trans. Comput. Biol. Bioinform., 2014

2013

Variational Bayes co-clustering with auxiliary information.

Motoki Shiga, Hiroshi Mamitsuka

Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, 2013

2012

A Variational Bayesian Framework for Clustering with Multiple Graphs.

Motoki Shiga, Hiroshi Mamitsuka

IEEE Trans. Knowl. Data Eng., 2012

Efficient semi-supervised learning on locally informative multiple graphs.

Motoki Shiga, Hiroshi Mamitsuka

Pattern Recognit., 2012

2011

Clustering genes with expression and beyond.

Motoki Shiga, Hiroshi Mamitsuka

WIREs Data Mining Knowl. Discov., 2011

A spectral approach to clustering numerical vectors as nodes in a network.

Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

Pattern Recognit., 2011

2009

Upper bound for variational free energy of Bayesian networks.

Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Mach. Learn., 2009

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data.

Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka

Bioinform., 2009

2008

Mining significant tree patterns in carbohydrate sugar chains.

Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka

Proceedings of the ECCB'08 Proceedings, 2008

2007

A spectral clustering approach to optimally combining numericalvectors with a modular network.

Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007

Annotating gene function by combining expression data with a modular gene network.

Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka

Proceedings of the Proceedings 15th International Conference on Intelligent Systems for Molecular Biology (ISMB) & 6th European Conference on Computational Biology (ECCB), 2007

2006

Upper Bounds for Variational Stochastic Complexities of Bayesian Networks.

Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006