研究概要

主要論文

• 1. T.Tsuda, M.Imaizumi.:
  "Benign Overfitting of Non-Sparse High-Dimensional Linear Regression with Correlated Noise"
  Electronic Journal of Statistics, 18(2), (2024).
• 2. S.Nakakita, P.Alquier, M.Imaizumi.:
  "Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution"
  Electronic Journal of Statistics, 18(1), (2024).
• 3. J.Komiyama, M.Imaizumi.:
  "High-dimensional Contextual Bandit Problem without Sparsity"
  Advances in Neural Information Processing Systems, 36, (2023).
• 4. R.Zhang, M.Imaizumi, B.Schölkopf, K.Muandet.:
  "Instrumental Variable Regression via Kernel Maximum Moment Loss"
  Journal of Causal Inference, 11(1), (2023).
• 5. M.Imaizumi, J.Schmidt-Hieber.:
  "On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization"
  IEEE Transaction on Information Theory, 69(2), (2023).
• 6. M.Imaizumi, K.Fukumizu.:
  "Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurface"
  Journal of Machine Learning Research, 23(111), (2022),
• 7. R.Nakada, M.Imaizumi.:
  "Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality"
  Journal of Machine Learning Research 21(174), (2020).
• 8. M.Imaizumi, K.Fukumizu.:
  "Deep Neural Networks Learn Non-Smooth Functions Effectively"
  PMLR: Artificial Intelligence and Statistics, (2019).
• 9. M.Imaizumi, T.Maehara, Y.Yoshida.:
  "Statistically Efficient Estimation for Non-Smooth Probability Densities"
  PMLR: Artificial Intelligence and Statistics, (2018).
• 10. M.Imaizumi, T.Maehara, K.Hayashi.:
  "On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm"
  Advances in Neural Information and Processing Systems 30, (2018).

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