HIDETOSHI SHIMODAIRA

Professor, Graduate School of Informatics, Kyoto University
Team Leader, RIKEN Center for Advanced Intelligence Project[ shimo@i.kyoto-u.ac.jp½ Kyoto, Japan https://orcid.org/0000-0002-3371-7724

SEP. 26, 2019

® http://stat.sys.i.kyoto-u.ac.jp/¯ https://www.linkedin.com/in/shimo/ https://scholar.google.com/citations?user=LvoIaIsAAAAJ

EXPERIENCERESEARCH

Professor, Graduate School of Informatics, Kyoto
University

Multiscale Bootstrap and Post-Selection Inference Apr 2017 – Present½ Kyoto, Japan Sep 2016 – Present½ Tokyo, Japan

Team Leader, RIKEN Center for Advanced Intelligence Project
Professor, Graduate School of Engineering Science, Osaka University Apr 2012 – Mar 2017

½ Osaka, Japan

 May 2005 - Mar 2012

½ Tokyo, Japan Jun 2002 - Apr 2005

½ Tokyo, Japan

 Jul 1999 - Jan 2001

½ Stanford, CA

Associate Professor, Department of Mathematicaland Computing Sciences, Tokyo Institute of Technology
Lecturer, Department of Mathematical and Computing Sciences, Tokyo Institute of Technology
Visiting Scholar, Department of Statistics, Stanford University
Assistant Professor, Department of Prediction
and Control, Institute of Statistical Mathematics
 Jul 1996 - May 2002

½ Tokyo, Japan Oct 1995 - Mar 1996

½ Seattle, WA Apr 1995 - Jun 1996

½ Tokyo, Japan

Visiting Scholar, Department of Genetics, University of Washington
Research Fellow of JSPS, University of TokyoEDUCATION• The method has been widely used in life sciences• Paper citation is about 9300 for our ﬁrst four papers• Higher-order asymptotic theory has been developed inlater research• Now, the method is extended to “selective inference”(aka post-selection inference) for hypotheses selected
after looking at data

Transfer Learning via Covariate Shift• The seminal paper (Shimodaira 2000) has been citednearly 1000

• This paper developed the framework of transfer learning with density ratio, and ﬁrst coined the terminology“covariate shift”

Multi-view Representation Learning• Theory and methods for graph embedding with neuralnetworks• Applications to natural language processing and image
recognition

SOFTWARE
pvclust• An R package for hierarchical clustering with p-values• Suzuki and Shimodaira (2002) [1549 citations]• https://cran.r-project.org/web/packages/
pvclust/index.html

Ph.D., Mathematical Engineering and Information
Physics, The University of Tokyo 1995• A statistical theory and method for computing conﬁdence measure of hypotheses via “scaling law” of bootstrap probabilities (i.e., the change of conﬁdence value
when changing the sample size)½ Tokyo, Japan

M.S., Mathematical Engineering and Information
Physics, The University of Tokyo 1992½ Tokyo, Japan 1990½ Tokyo, Japan

B.S., Mathematical Engineering and Information
Physics, The University of TokyoCONSEL• CONSEL: for assessing the conﬁdence of phylogenetic
tree selection• Shimodaira and Hasegawa (2001) [1930 citations]• http://stat.sys.i.kyoto-u.ac.jp/prog/consel/

scaleboot• scaleboot: Approximately Unbiased P-values via Multiscale Bootstrap• Shimodaira (2008)• https://cran.r-project.org/web/packages/scaleboot/index.html

PAFit• PAFit: Generative Mechanism Estimation in Temporal
Complex Networks• T. Pham, P. Sheridan and H. Shimodaira (2015)• https://cran.r-project.org/web/packages/PAFit/index.html

SELECTED PUBLICATIONS
Multi-view Representation Learning and Natural
Language Processing• A. Okuno and H. Shimodaira, Hyperlink Regression via
Bregman Divergence, arXiv 2019.
• G. Kim, A. Okuno, K. Fukui and H. Shimodaira, Representation Learning with Weighted Inner Product for
Universal Approximation of General Similarities, Proceedings of 28th International Joint Conference on
Artiﬁcial Intelligence (IJCAI-19), 5031–5038, 2019.
(acceptance rate 17.8%)• G. Kim, K. Fukui and H. Shimodaira, Segmentation-freecompositional n-gram embedding, Proceedings of 2019
Annual Conference of the North American Chapter ofthe Association for Computational Linguistics (NAACLHLT), 3207–3215, 2019. (acceptance rate long+short
approx. 22%)
• A. Okuno and H. Shimodaira, Robust Graph Embedding with Noisy Link Weights, Proceedings of the 22ndInternational Conference on Artiﬁcial Intelligence andStatistics (AISTATS), PMLR 89, 664–673, 2019. (acceptance rate 32.4%)• A. Okuno, G. Kim and H. Shimodaira, Graph Embedding
with Shifted Inner Product Similarity and Its Improved
Approximation Capability, Proceedings of the 22nd International Conference on Artiﬁcial Intelligence andStatistics (AISTATS), PMLR 89, 644–653, 2019. (acceptance rate 32.4 %)• A. Okuno, T. Hada and H. Shimodaira, A probabilistic
framework for multi-view feature learning with manyto-many associations via neural networks, Proceedings of the 35th International Conference on Machine
Learning (ICML), PMLR 80, 3888–3897, 2018. (acceptance rate 25.1%)• K. Fukui, A. Okuno and H. Shimodaira, Image and tag
retrieval by leveraging image-group links with multidomain graph embedding, 2016 IEEE International
Conference on Image Processing (ICIP), 221–225,
2016.
• T. Oshikiri, K. Fukui and H. Shimodaira, Cross-Lingual
Word Representations via Spectral Graph Embeddings,
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 493–498,
2016.
• H. Shimodaira, Cross-validation of matching correlation analysis by resampling matching weights, NeuralNetworks, 75, 126–140, 2016.

Network Science• T. Pham, P. Sheridan and H. Shimodaira, PAFit: an R
Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal
Complex Networks, Journal of Statistical Software,
accepted, 2019.
• M. Inoue, T. Pham and H. Shimodaira, Transitivity vsPreferential Attachment: Determining the Driving
Force Behind the Evolution of Scientiﬁc Co-Authorship
Networks, Unifying Themes in Complex Systems IX.
ICCS 2018, Springer Proceedings in Complexity, 262–271, 2018
• T. Pham, P. Sheridan and H. Shimodaira, Joint estimation of preferential attachment and node ﬁtness
in growing complex networks, Scientiﬁc Reports, 6,32558, 2016.
• T. Pham, P. Sheridan and H. Shimodaira, PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks, PLoS ONE, 10,e0137796, 2015.

Multiscale Bootstrap and Post-Selection Inference• Y. Terada and H. Shimodaira, Selective inference after
variable selection via multiscale bootstrap, arXiv 2019.
• H. Shimodaira and Y. Terada, Selective Inference for
Testing Trees and Edges in Phylogenetics, Frontiersin Ecology and Evolution, 10.3389/fevo.2019.00174,2019.
• Y. Terada and H. Shimodaira, Selective inference forthe problem of regions via multiscale bootstrap, arXiv2017.
• H. Shimodaira, Higher-order accuracy of multiscaledouble bootstrap for testing regions, Journal of Multivariate Analysis, 130, 208–223, 2014.
• H. Shimodaira, Testing regions with nonsmooth boundaries via multiscale bootstrap, Journal of Statistical
Planning and Inference, 138, 1227–1241, 2008.
• R. Suzuki and H. Shimodaira, Pvclust: an R package
for assessing the uncertainty in hierarchical clustering,
Bioinformatics, 22, 1540-1542, 2006. (Google ScholarCitations 1549)• H. Shimodaira, Approximately unbiased tests of regions
using multistep-multiscale bootstrap resampling, The
Annals of Statistics, 32, 2616–2641, 2004. (GoogleScholar Citations 304)
• H. Shimodaira, An approximately unbiased test of phylogenetic tree selection, Systematic biology, 51, 492508, 2002. (Google Scholar Citations 2015)• H. Shimodaira and M. Hasegawa, CONSEL: for assessing the conﬁdence of phylogenetic tree selection,
Bioinformatics, 17, 1246–1247, 2001. (Google ScholarCitations 1930)• H. Shimodaira, Multiple comparisons of log-likelihoods
and combining nonnested models with applications
to phylogenetic tree selection, Communications in
Statistics-Theory and Methods, 30, 1751–1772, 2001.
• H. Shimodaira and M. Hasegawa, Multiple comparisonsof log-likelihoods with applications to phylogenetic
inference, Molecular Biology and Evolution,16, 1114–1116, 1999. (Google Scholar Citations 3840)

Covariate Shift and Information Criterion• Imori and H. Shimodaira, An Information Criterion for
Auxiliary Variable Selection in Incomplete Data Analysis, Entropy, 21, 281, 2019.
• H. Shimodaira and H. Maeda, An information criterion
for model selection with missing data via completedata divergence, Annals of the Institute of StatisticalMathematics, 70, 421–438, 2018.
• H. Shimodaira, Improving predictive inference undercovariate shift by weighting the log-likelihood function,
Journal of statistical planning and inference, 90, 227–244, 2000. (Google Scholar Citations 981)