Journal (refereed)
[27] Tomonari Sei and Keisuke Yano, Estimation of bivariate minimum information dependence models using the density-ratio framework, accepted in Annals of the Institute of Statistical Mathematics
[26*] Kosei Wada, Makoto Sasaki, and Keisuke Yano, Performance Evaluation of High-Dimensional Spatio-Temporal Evolution Simulation Using Physics-Informed Neural Networks, accepted in Journal of Plasma and Fusion Research. (*My contribution: technical comments & designing initial programs)
[25] Gourab Mukherjee and Keisuke Yano, Predictive inference in Linear mixed models, accepted in Indian Journal of Pure and Applied Mathematics.
[24] Masayuki Kano, Keisuke Yano, Yusuke Tanaka, Tetsuya Takabatake, Yusaku Ohta, Spatio-temporal characteristics in the GEONET F5 solution in the frequency domain estimated based on the robust spectral analysis, Earth Planets Space 77, 103, 2025 (DOI: https://doi.org/10.1186/s40623-025-02236-3)
[23] Yukito Iba and Keisuke Yano, Posterior Covariance Information Criterion for general loss functions, Bayesian Analysis, 2025 (DOI:https://doi.org/10.1214/25-BA1536, arxiv: arXiv:2206.05887)
[22] Atsushi Takahashi, Keisuke Yano, and Masayuki Kano, A GNSS-velocity clustering method applicable from local to global scales, Journal of Geophysical Research: Solid Earth, vol.130, 2025 (DOI: http://dx.doi.org/10.1029/2024JB029689)
[21*] Yusuke Tanaka, Masayuki Kano, and Keisuke Yano, Detecting Slow Slip Signals in Southwest Japan Based on Machine Learning Trained by Real GNSS Time Series, Journal of Geophysical Research: Solid Earth , vol.130, 2025 (DOI: https://doi.org/10.1029/2024JB029499) (*My contribution: discussions on the analysis methods)
[20*] Taiga Saito, Yu Otake, Stephen Wu, and Keisuke Yano, Exploring high-order multivariate geotechnical features using the minimum information dependence model, Geodata and AI, 2025, 100009. (*My contribution: technical comments & designing initial programs)
[19] Michiko Okudo and Keisuke Yano, Matching prior pairs connecting Maximum A Posteriori estimation and posterior expectation, Bayesian Analysis, 2024 (DOI:https://doi.org/10.1214/24-BA1500 ; https://arxiv.org/abs/2312.09586)
[18] Hideitsu Hino and Keisuke Yano, An embedding structure of determinantal point process, Information Geometry , vol. 7, 523-542, 2024 (DOI: https://doi.org/10.1007/s41884-024-00156-x; https://arxiv.org/abs/2404.11024; Corrigendum)
[17*] Yusuke Tanaka, Masayuki Kano, Takuya Nishimura, Yusaku Ohta, Keisuke Yano, Numerical experiment toward simultaneous estimation of the spatiotemporal evolution of interseismic fault slips and block motions in southwest Japan, Earth, Planets and Space, vol. 76, 2024 (https://doi.org/10.1186/s40623-024-02009-4) (*My contribution: helping the initial processing of the data and mathematical discussion of the analysis)
[16] Tomonari Sei and Keisuke Yano, Minimum information dependence modeling, Bernoulli, vol. 30, 2623-2643, 2024 (DOI: 10.3150/23-BEJ1687; arXiv:2206.06792)
[15] Akifumi Okuno and Keisuke Yano, A generalization gap estimation for overparameterized models via Langevin functional variance, Journal of Computational and Graphical Statistics, vol. 32, 1287-1295, 2023 (https://doi.org/10.1080/10618600.2023.2197488; arXiv:2112.03660)
[14] Yukito Iba and Keisuke Yano, Posterior Covariance Information Criterion for Weighted Inference, Neural Computation, vol. 35, 1340–1361, 2023 (https://doi.org/10.1162/neco_a_01592, arXiv:2106.13694)
[13] Akifumi Okuno and Keisuke Yano, Dependence of variance on covariate design in nonparametric link regression, Statistics and Probability Letters, vol. 193, 2023, 109716 (https://doi.org/10.1016/j.spl.2022.109716; arXiv:2012.13106)
[12] Keisuke Yano and Masayuki Kano, l1 Trend Filtering-based Detection of Short-term Slow Slip Events: Application to a GNSS Array in Southwest Japan, Journal of Geophysical Research: Solid Earth, vol. 127, 5, e2021JB023258, 2022 (https://doi.org/10.1029/2021JB023258;python code)
[11] Yohta Yamanaka, Sumito Kurata, Keisuke Yano, Fumiyasu Komaki, Takahiro Shiina, Aitaro KatoStructured regularization based local earthquake tomography for the adaptation to velocity discontinuities, Earth, Planets and Space, vol. 74, 43, 2022 (https://doi.org/10.1186/s40623-022-01600-x)
[10*] Hidenobu Takahashi, Kazuya Tateiwa, Keisuke Yano, Masayuki Kano, A convolutional neural network-based classification of local earthquakes and tectonic tremors in Sanriku-oki, Japan, using S-net data, Earth, Planets and Space, vol. 73, 186, 2021 (https://doi.org/10.1186/s40623-021-01524-y) (*My contribution: technical comments & designing initial programs)
[9] Keisuke Yano, Takahiro Shiina, Sumito Kurata, Aitaro Kato, Fumiyasu Komaki, Shin'ichi Sakai, and Naoshi Hirata, Graph-partitioning based convolutional network for earthquake detection using a seismic array, Journal of Geophysical Research: Solid Earth, vol. 126, e2020JB020269, 2021 (https://doi.org/10.1029/2020JB020269) Top downloaded article (2023/3/30) among work published in an issue between 1 Jan. 2021-31 Dec. 2021.
[8] Edward George, Gourab Mukherjee, and Keisuke Yano, Optimal Shrinkage Estimation of Predictive Densities under $\alpha$-divergences, Bayesian Analysis, vol.16, 1139-1155, 2021 (https://doi.org/10.1214/21-BA1264)
[7] Keisuke Yano, Ryoya Kaneko, and Fumiyasu Komaki, Minimax Predictive Density for Sparse Count Data, Bernoulli, vol. 27, 1212-1238, 2021 (doi:10.3150/20-BEJ1271 ; python code)
[6] Kento Nakamura, Keisuke Yano, and Fumiyasu Komaki, Adjacency-based regularization for partially ranked data with non-ignorable missing, Computational Statistics & Data Analysis, vol. 145, 106905, 2020 (doi:10.1016/j.csda.2019.106905)
[5] Keisuke Yano and Kengo Kato, On frequentist coverage errors of Bayesian credible sets in moderately high dimensions , Bernoulli, vol. 26, pp.616-641, 2020 (arXiv:1803.03450 ; doi: 10.3150/19-BEJ1142 )
[4] Yuya Takasu, Keisuke Yano, and Fumiyasu Komaki, Scoring Rules for Statistical Models on Spheres, Statistics and Probability Letters, vol. 138, pp. 111-115, 2018 (doi:10.1016/j.spl.2018.02.054).
[3] Keisuke Yano and Fumiyasu Komaki, Asymptotically minimax prediction in infinite sequence models, Electronic Journal of Statistics, vol. 11,pp. 3165-3195, 2017 (doi:10.1214/17-EJS1312).
[2] Keisuke Yano and Fumiyasu Komaki, Information criteria for prediction when the distributions of current and future observations differ, Statistica Sinica, vol. 27, pp. 1205-1223, 2017 (doi:10.5705/ss202015.0380).
[1] Keisuke Yano and Fumiyasu Komaki, Asymptotically constant-risk predictive densities when the distributions of data and target variables are different, Entropy, vol. 16, pp. 3026-3048, 2014 (doi:10.3390/e16063026).
International Conference (Refereed)
Takashi Nakada, Shinobu Miwa, Keisuke Yano and Hiroshi Nakamura, Performance Modeling for Designing NoC-based Multiprocessors, Proceedings of IEEE International Symposium on Rapid System Prototyping, pp.30-36, Montreal, Canada, Oct 3-4, 2013 (doi:10.1109/RSP.2013.6683955).
Preprint
Universality of intermittencies by trapped coherent vortices in a linear magnetized plasma device (with Makoto Sasaki, Yuichi Kawachi, Takuma Yamada, Yusuke Kosuga, and Akihide Fujisawa; submitted)
Recurrence analysis on slow slip events in Japan using the Brownian Passage Time distribution (with Masayuki Kano, Takane Hori, and Keisuke Ariyoshi; submitted)
Towards a robust frequency-domain analysis: Spectral R\'{e}nyi divergence revisited (with Tetsuya Takabatake; arXiv:2310.06902 ; in revision)
Misc.
Non-asymptotic Bayesian Minimax Adaptation (Keisuke Yano and Fumiyasu Komaki) (arXiv:1609.00940)
On $\varepsilon$-admissibility in High Dimension and Nonparametrics (Keisuke Yano and Fumiyasu Komaki) (arXiv:1708.03751)
International Conference (Invited before 2020)
Risk-estimation based predictive densities for heteroskedastic hierarchical models , ICSA 2019, China, 21 December (20-22 December), 2019.
On estimation and prediction for high-dimensional Poisson models with quasi-zero inflation, CMStatistics 2019 , UK, 14 December (14-16 December), 2019.
On frequentist coverage errors of Bayesian credible sets in moderately high dimensions, Italy, 24 July (22-26 July), 2019.
The Berry--Esseen type bound for the Bernstein--von Mises theorem in moderately high dimensions, EAC-ISBA 2019, Japan, 13 July (13-14 July), 2019.
On the construction of adaptive predictive densities for sparse count data , EcoSta 2019, Taiwan, 26 June (25-27 June), 2019.
Adaptive minimax predictive density for sparse Poisson models, Canada, 11 April, 2019.
Shrinkage priors for nonparametric estimations, CFE-SMStatistics 2016, Spain, 9-11 December, 2016.
競争的研究資金
2023-2025年度 基盤研究(C) 「時空間構造をもつデータに関する推定不確実性評価法と予測評価法の構築」
2021-2026年度 学術変革領域研究(A) 「情報科学と地球物理学の融合によるSlow-to-Fast地震現象の包括的理解」 研究分担者
2021-2024年度 基盤研究(C) 「予測概念の多様性に対応した情報量規準の開発：計算統計的アプローチ」 研究分担者
2019-2023年度 若手研究「構造制約に着目した高次元カウントデータの未知母数推定法と不確実性評価法の構築」
2017-2018年度 研究活動スタート支援「高次元状況下の転移学習における高速かつ高精度な分布予測手法の開発」
2015-2016年度 特別研究員奨励費「ミニマックスなベイズ予測分布の構成法とモデル選択への応用」
受賞
2022年度日本統計学会小川研究奨励賞
2013年度統計関連学会連合大会最優秀報告賞
招待講演 (Before 2022)
1. 矢野 恵佑 (2022.11.5). Minimum information dependence modeling for mixed domain data, 2022年度科学研究費シンポジウム 大規模複雑データの理論と方法論～新たな発展と関連分野への応用～, つくば国際会議場,口頭発表(招待講演).
2. 矢野恵佑 (2022.9.6) 高次元・無限次元モデルにおける予測分布, 日本統計学会各賞受賞者記念講演.
3. 矢野恵佑 (2021.9.16) 予測分布論の最近の進展, 日本数学会(特別講演).
4. Yano, K. (2021.12.19) On estimating generalization gaps via the functional variance in overparameterized models, CMStatistics2021(招待講演).
5. 矢野恵佑 (2021.12.11) 予測の情報量規準, 情報計測オンラインセミナー(招待講演).
6. 矢野恵佑 (2019. 11.15 )広島大学 広島統計グループ金曜セミナー
7. 矢野恵佑 (2019. 10.10)高次元・無限次元の自由度をもつ統計モデルにおけるベイズ統計学, 愛媛大学 数学座談会 (DSシリーズ第8回)