1 code implementation • 30 Aug 2023 • Masaaki Takada, Hironori Fujisawa
This paper presents a comprehensive exploration of the theoretical properties inherent in the Adaptive Lasso and the Transfer Lasso.
no code implementations • NeurIPS 2020 • Masaaki Takada, Hironori Fujisawa
The proposed method has a tight estimation error bound under a stationary environment, and the estimate remains unchanged from the source estimate under small residuals.
no code implementations • 1 Nov 2018 • Masaaki Takada, Hironori Fujisawa, Takeichiro Nishikawa
Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved.
no code implementations • 6 Nov 2017 • Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features.