no code implementations • 15 Apr 2024 • Takashi Takahashi
Under-bagging (UB), which combines under sampling and bagging, is a popular ensemble learning method for training classifiers on an imbalanced data.
no code implementations • 7 Feb 2024 • Koki Okajima, Takashi Takahashi
This study investigates the asymptotic dynamics of alternating minimization applied to optimize a bilinear non-convex function with normally distributed covariates.
no code implementations • 30 Jun 2023 • Siqi Na, Yoshiyuki Kabashima, Takashi Takahashi, Tianyao Huang, Yimin Liu, Xiqin Wang
Based on this estimator, we construct a detector, termed the debiased weighted LASSO detector (DWLD), for CS radar systems and prove its advantages.
no code implementations • 25 Feb 2023 • Koki Okajima, Xiangming Meng, Takashi Takahashi, Yoshiyuki Kabashima
The obtained bound for perfect support recovery is a generalization of that given in previous literature, which only considers the case of Gaussian noise and diverging $d$.
no code implementations • 30 Sep 2022 • Siqi Na, Tianyao Huang, Yimin Liu, Takashi Takahashi, Yoshiyuki Kabashima, Xiqin Wang
Such detector can analytically provide the threshold according to given false alarm rate, which is not possible with the conventional CS detector, and the detection performance is proved to be better than that of the traditional LASSO detector.
no code implementations • 16 May 2022 • Takashi Takahashi
When the number of iterations is small, ST improves generalization performance by fitting the model to relatively reliable pseudo-labels and updating the model parameters by a large amount at each iteration.
no code implementations • 19 Mar 2020 • Takashi Takahashi, Yoshiyuki Kabashima
We consider the variable selection problem of generalized linear models (GLMs).
no code implementations • 23 May 2019 • Takashi Takahashi, Yoshiyuki Kabashima
Resampling techniques are widely used in statistical inference and ensemble learning, in which estimators' statistical properties are essential.