Research keywords (5)：
statistical learning theory
, high dimensional statistics
, kernel method
, stochastic optimization
, Bayesian statistics
Research theme for competitive and other funds (10)：
2020 - 2025 Innovative Developments of Theories and Methodologies for Large Complex Data
2018 - 2022 Intensifying deep learning theory and its application to structure analysis of deep neural network
2018 - 2021 Advance of artificial intelligence by theoretical investigation of deep learning
2015 - 2020 Establishing Theoretical Foundations for Mathematical Modeling of Pathological Biosystems and its Applications to Personalized Medicine
2015 - 2020 Theories and Methodologies for Large Complex Data
2014 - 2019 Thery and methods for high dimensional data analysis with internal structure
2013 - 2018 Deepening and applications of sparse modeling by approaches of semiparametric Bayesian inference
2013 - 2018 Theories of structured estimation methods for large scale data and their applications
2010 - 2012 Theory and applications of cross-data-type machine learning methods
2006 - 2009 学習理論の研究と知能情報処理への応用
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Papers (29)：
Hiroaki Kingetsu, Kenichi Kobayashi, Taiji Suzuki. Neural Network Module Decomposition and Recomposition with Superimposed Masks. 2023 International Joint Conference on Neural Networks (IJCNN). 2023
Stefano Massaroli, Michael Poli, Sho Sonoda, Taiji Suzuki, Jinkyoo Park, Atsushi Yamashita, Hajime Asama. Differentiable Multiple Shooting Layers. Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021(NeurIPS). 2021. 16532-16544
Shaogao Lv, Zengyan Fan, Heng Lian, Taiji Suzuki, Kenji Fukumizu. A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model. Computational Statistics and Data Analysis. 2020. 152
Song Liu, Taiji Suzuki, Raissa Relator, Jun Sese, Masashi Sugiyama, Kenji Fukumizu. SUPPORT CONSISTENCY OF DIRECT SPARSE-CHANGE LEARNING IN MARKOV NETWORKS. ANNALS OF STATISTICS. 2017. 45. 3. 959-990
Song Liu 0002, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu. Trimmed Density Ratio Estimation. Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA. 2017. 4518-4528
Song Liu, Kenji Fukumizu, Taiji Suzuki. Learning sparse structural changes in high-dimensional Markov networks: A review on methodologies and theories. Behaviormetrika. 2017. 44. 1. 265-286
Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu. Structure learning of partitioned Markov networks. 33rd International Conference on Machine Learning, ICML 2016. 2016. 1. 657-671
金川 平志郎, 鈴木 大慈. Non-parametric tensor learning with Gaussian process prior and its application to multi-task learning (情報論的学習理論と機械学習 情報論的学習理論ワークショップ(IBIS2015)). 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報. 2015. 115. 323. 273-280
Not Too Late to Learn! Mathematics for Computer Science:3. Mathematics for Machine Learning. IPSJ Magazine. 2015. 56. 5. 442-447
