2 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
Masaaki Takada, Hironori Fujisawa
This paper presents a comprehensive exploration of the theoretical properties inherent in the Adaptive Lasso and the Transfer Lasso. The Adaptive Lasso, a well-established method,…
Transfer Learning via Regularization
Masaaki Takada, Hironori Fujisawa
Machine learning algorithms typically require abundant data under a stationary environment. However, environments are nonstationary in many real-world applications. Critical issues…
HMLasso: Lasso with High Missing Rate
Masaaki Takada, Hironori Fujisawa, Takeichiro Nishikawa
Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often…
Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization
Takayuki Kawashima, Hironori Fujisawa
The stochastic gradient descent has been widely used for solving composite optimization problems in big data analyses. Many algorithms and convergence properties have been develope…