2025.4 T. Matsuda. Matrix estimation via singular value shrinkage. Memorial Conference for Professor William Strawderman, Rutgers University.
2025.3 T. Matsuda. Information geometry of Wasserstein statistics. Further Developments of Information Geometry 2025, Tokyo.
2025.3 T. Matsuda and T. Soma. Information geometry of operator scaling. Further Developments of Information Geometry 2025, Tokyo.
2025.3 H. Nishimori and T. Matsuda. On the attainment of the Wasserstein--Cramer--Rao lower bound. Further Developments of Information Geometry 2025, Tokyo.
2025.3 T. Nakagawa, T. Matsuda and K. Tahata. On Partitioning of Goodness-of-fit statistics for Symmetry in contingency tables from the viewpoint of Information Geometry. Further Developments of Information Geometry 2025, Tokyo.
2025.1 T. Matsuda. Analysis of kimariji in competitive karuta: sequential Bayes and reservoir computing. The 3rd RIKEN CBS Co-Creation International Conference, Wako.
2024.12 I. Sukeda and T. Matsuda. Torus graph modelling for EEG data analysis. ICSDS 2024, Nice.
2024.12 H. Nishimori and T. Matsuda. On the attainment of the Wasserstein--Cramer--Rao lower bound. CMStatistics 2024, London.
2024.11 R. Li, T. Matsuda and H. Yanaka. Exploring intra and inter-language consistency in embeddings with ICA. 2024 conference on Empirical Methods in Natural Language Processing (EMNLP 2024), Miami.
2024.9 I. Sukeda and T. Matsuda. Torus graph modelling for EEG analysis. Fall School Time Series, Random Fields and beyond, Ulm University.
2024.7 T. Matsuda. Adapting to general quadratic loss via singular value shrinkage. ProbNum24, London.
2024.7 T. Matsuda. Double shrinkage priors for a normal mean matrix. 2nd Joint Conference on Statistics and Data Science in China (JCSDS 2024), Kunming.
2024.6 T. Matsuda. Matrix estimation and prediction via singular value shrinkage. Tartu Conference on Multivariate Statistics 2024, Tartu.
2024.6 T. Matsuda. Estimation and selection of non-normalized models. Australian National University.
2024.1 T. Matsuda. Oscillator decomposition of time series data. Complex systems seminar, University of Sydney.
2024.1 T. Matsuda. Wasserstein--Cramer--Rao inequality and robustness. 6th IMS Asia Pacific Rim Meeting (ims-APRM 2024), Melbourne.
2023.7 T. Matsuda. Matrix estimation via singular value shrinkage. 9th International Forum in Statistics, Beijing.
2022.9 T. Matsuda, S. Baba and A. Kato. Data-driven decomposition of slow-to-fast earthquakes. International Joint Workshop on Slow Earthquakes 2022, Nara.
2022.6 T. Matsuda. Adapting to arbitrary quadratic loss via singular value shrinkage. EcoSta 2022, Kyoto.
2022.4 T. Matsuda. Oscillator decomposition of time series data. PennSIVE Seminar, University of Pennsylvania.
2022.4 T. Matsuda. Matrix estimation by singular value shrinkage. Conference on Advances in Bayesian and Frequentist Theory and Methods with a Celebration of Bill Strawderman's 80th Birthday, Rutgers University. (link)
2021.11 T. Matsuda. Estimation under matrix quadratic loss and matrix superharmonic priors. EAC-ISBA 2021, online.
2021.9 T. Matsuda. Oscillator decomposition of time series data. International Joint Workshop on Slow Earthquakes 2021, online.
2021.7 W. Xu and T. Matsuda. Interpretable Stein goodness-of-fit tests on Riemannian manifolds. 38th International Conference on Machine Learning (ICML 2021), online.
2021.7 S. Amari and T. Matsuda. Wasserstein statistics in one-dimensional location-scale models. 5th conference on Geometric Science of Information (GSI 2021), online.
2021.7 W. Xu and T. Matsuda. On Geometry of Stein Goodness-of-fit Testing. 5th conference on Geometric Science of Information (GSI 2021), online.
2021.6 T. Matsuda. Matrix superharmonic priors for Bayes estimation under matrix quadratic loss. World Meeting of the International Society for Bayesian Analysis (ISBA 2021), online.
2021.6 T. Matsuda. Estimation under matrix quadratic loss and matrix superharmonicity. EcoSta 2021, online.
2020.6 W. Xu and T. Matsuda. A Stein goodness-of-fit test for directional distributions. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo.
2020.6 M. Uehara, T. Matsuda and J. K. Kim. Imputation estimators for unnormalized models with missing data. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo.
2020.6 M. Uehara, T. Kanamori, T. Takenouchi and T. Matsuda. A unified estimation framework for unnormalized models with statistical efficiency. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo.
2019.12 T. Matsuda. Estimation of ODE models with discretization error quantification. Oxford Numerical Analysis Seminar, Oxford.
2019.11 T. Matsuda. Estimation and selection of non-normalized models. Oxford Statistics Department Seminar, Oxford.
2019.9 T. Matsuda. Singular value shrinkage prior: a matrix version of Stein's prior. Stanford Statistics Department Seminar, Stanford.
2019.7 T. Matsuda and Y. Miyatake. Estimation of ODE models with quantifying discretization error. SciCADE 2019, Austria.
2019.7 T. Matsuda. Singular value shrinkage priors for Bayesian prediction. EAC-ISBA 2019, Kobe.
2019.6 T. Matsuda. Singular value shrinkage prior: a matrix version of Stein's prior. Symposium in Memory of Charles Stein, Singapore. (slide)
2019.4 T. Matsuda and A. Hyvarinen. Estimation of Non-Normalized Mixture Models. 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Okinawa. (poster)
2019.4 T. Matsuda. Singular value shrinkage priors and empirical Bayes matrix completion. New and Evolving Roles of Shrinkage in Large-Scale Prediction and Inference, Banff. (video)
2019.2 T. Matsuda. Oscillator decomposition of time series data. ICMMA 2018, Tokyo.
2018.10 T. Matsuda, F. Homae, H. Watanabe, G. Taga and F. Komaki. Statistical verification of the common oscillatory behaviors in oxy-Hb and deoxy-Hb time series. fNIRS 2018, Tokyo.
2018.6 T. Matsuda. Minimax adaptive reduced-rank regression. 5th IMS Asia Pacific Rim Meeting (ims-APRM 2018), Singapore.
2016.12 T. Matsuda and F. Komaki. Improvement of singular value shrinkage priors and block-wise Stein priors. 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016), Seville.
2016.6 T. Matsuda and F. Komaki. Decomposing time series into oscillation components with random frequency modulation. 4th IMS Asia Pacific Rim Meeting (ims-APRM 2016), Hong Kong.
2015.6 T. Matsuda and F. Komaki. Singular value shrinkage priors for Bayesian prediction. 11th International Workshop on Objective Bayes Methodology (O-Bayes15), Valencia.
2014.7 T. Matsuda and F. Komaki. Singular value shrinkage priors for Bayesian prediction. 3rd IMS Asia Pacific Rim Meeting (ims-APRM 2014), Taipei.