Publication

• Papers:

  • • Guan, X.* and Terada, Y. (2023+). Sparse kernel k-means for high-dimensional data, to appear in Pattern Recognition. [Link]

     *She is my PhD student.

• • • Terada, Y. and Shimodaira, H. (2023). Selective inference after feature selection via multiscale bootstrap, Annals of the Institute of Statistical Mathematics, 75, 99–125. [Link]

• • • Hirose, K. and Terada, Y. (2022+).  Simple structure estimation via prenet penalization, Psychometrika. [Link]

• • • Zhang, B.*, Chen, J., and Terada, Y.* (2021). Dynamic visualization for L1 fusion convex clustering in near-linear time, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI2021), PMLR 161, 515–524. [Link]

*These authors contributed mainly to this work. 

• Morikawa, K., Nagao, H., Ito, S., Terada, Y., Sakai, S., and Hirata, N. (2021). Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression, Geophysical Journal International, 226 (2), 1018–1035. [Link]

• • • Terada, Y., Ogasawara, I., and Nakata, K. (2020). Classification from only positive and unlabeled functional data, Annals of Applied Statistics, 14 (4), 1724–1742. [Link]

*A part of this research was featured on NHK News (Ohayō Nippon).

• • • Poignard, B. and Terada, Y. (2020). Sparse Approximate Factor Models: Non-Asymptotic Properties, Electronic Journal of Statistics, 14, 3315–3365. [Link]

• • • Terada, Y. and Hirose, R. (2020). Fast generalization error bound of deep learning without scale invariance of activation functions, Neural Networks, 129, 344–358. [Link]

• • • Lim, J.N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., and Shimodaira, H. (2020). More Powerful Selective Kernel Tests for Feature Selection, In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), PMLR 108, pp. 820–830. [Link]

• • • Terada, Y. and Yamamoto, M. (2019). Kernel Normalized Cut: a Theoretical Revisit, In Proceedings of the 36th International Conference on Machine Learning (ICML2019), eds., K. Chaudhuri and R. Salakhutdinov, PMLR 97, pp. 6206–6214. [Link][Poster]

• • • Shimodaira, H. and Terada, Y. (2019). Selective inference for testing trees and edges in phylogenetics, Frontiers in Ecology and Evolution, 7:174, 1–15. DOI: 10.3389/fevo.2019.00174

• • • Amano, K., Lang, Q., Terada Y., and Nishida, S. (2016). Neural correlates of the time marker for the perception of event timing, eNeuro, 3 (4), 1–17. [Link]

• • • Terada, Y. (2015). Strong consistency of factorial k-means clustering, Annals of the Institute of Statistical Mathematics, 67 (2), 335–357.[Link]

• • • Yamamoto, M. and Terada, Y. (2014). Functional factorial k-means analysis, Computational Statistics & Data Analysis, 79, 133–148.[Link]

• • • Terada, Y. and von Luxburg, U. (2014). Local ordinal embedding, In Proceedings of the 31st International Conference on Machine Learning (ICML2014), eds., P. E. Xing and T. Jebara, PMLR 32, pp. 847–855. [Link][R package: loe][Errata]

• • • Terada, Y. (2014). Strong consistency of reduced k-means clustering, Scandinavian Journal of Statistics, 41 (4), 913–931.[Link]

• Preprints:

  • • Touw, D. J. W., Groenen, P. J. F., and Terada, Y. (2022) Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering. [arXiv:2211.01877]

    • Terada, Y. and Shimodaira, H. (2017).  Selective inference for the problem of regions via multiscale bootstrap. [arXiv:1711.00949]

    • Terada, Y. (2013). Clustering for high-dimension, low-sample size data using distance vectors. [arXiv:1312.3386]

• Old proceedings:

• • • Terada, Y. and Yadohisa, H. (2011). Multidimensional scaling with hyperbox model for percentile dissimilarities, In: Watada, J., Phillips-Wren, G., Jain, L. C., and Howlett, R. J. (Eds.): Intelligent Decision Technologies, Springer Verlag, 779-788. [Link]

    • Terada, Y. and Yadohisa, H. (2011). Multidimensional scaling with the nested hypersphere model for percentile dissimilarities, Procedia Computer Science, 6, 364-369. [Link]

    • Terada, Y. and Yadohisa, H. (2010). Non-hierarchical clustering for distribution-valued data. COMPSTAT 2010: Proceedings in Computational Statistics, Psysica-Verlag, Heidelberg, 1653-1660.[Link]