Motoki Shiga

Publication Activity (10 Years)

Years Active: 2006-2022
Publications (10 Years): 6

Top Topics

Auxiliary Information

Top Venues

IEEE ACM Trans. Comput. Biol. Bioinform.

Publications

Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy. Neural Comput. 34 (10) (2022)
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. ICML (2020)
Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, Motoki Shiga, Masayuki Karasuyama
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling. CoRR (2020)
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
Multi-fidelity Bayesian Optimization with Max-value Entropy Search. CoRR (2019)
Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
Direct conditional probability density estimation with sparse feature selection. Mach. Learn. 100 (2-3) (2015)
Motoki Shiga, Hiroshi Mamitsuka
Non-Negative Matrix Factorization with Auxiliary Information on Overlapping Groups. IEEE Trans. Knowl. Data Eng. 27 (6) (2015)
Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka
Detecting Differentially Coexpressed Genesfrom Labeled Expression Data: A Brief Review. IEEE ACM Trans. Comput. Biol. Bioinform. 11 (1) (2014)
Motoki Shiga, Hiroshi Mamitsuka
Variational Bayes co-clustering with auxiliary information. MultiClust@KDD (2013)
Motoki Shiga, Hiroshi Mamitsuka
Efficient semi-supervised learning on locally informative multiple graphs. Pattern Recognit. 45 (3) (2012)
Motoki Shiga, Hiroshi Mamitsuka
A Variational Bayesian Framework for Clustering with Multiple Graphs. IEEE Trans. Knowl. Data Eng. 24 (4) (2012)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
A spectral approach to clustering numerical vectors as nodes in a network. Pattern Recognit. 44 (2) (2011)
Motoki Shiga, Hiroshi Mamitsuka
Clustering genes with expression and beyond. WIREs Data Mining Knowl. Discov. 1 (6) (2011)
Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. Bioinform. 25 (21) (2009)
Kazuho Watanabe, Motoki Shiga, Sumio Watanabe
Upper bound for variational free energy of Bayesian networks. Mach. Learn. 75 (2) (2009)
Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka
Mining significant tree patterns in carbohydrate sugar chains. ECCB (2008)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
Annotating gene function by combining expression data with a modular gene network. ISMB/ECCB (Supplement of Bioinformatics) (2007)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
A spectral clustering approach to optimally combining numericalvectors with a modular network. KDD (2007)
Kazuho Watanabe, Motoki Shiga, Sumio Watanabe
Upper Bounds for Variational Stochastic Complexities of Bayesian Networks. IDEAL (2006)