2 citations · 3 across the 2 of their papers we have counts for
5 papers
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…
Robust and sparse Gaussian graphical modeling under cell-wise contamination
Shota Katayama, Hironori Fujisawa, Mathias Drton
Graphical modeling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this t…
Sparse and Robust Linear Regression: An Optimization Algorithm and Its Statistical Properties
Shota Katayama, Hironori Fujisawa
This paper studies sparse linear regression analysis with outliers in the responses. A parameter vector for modeling outliers is added to the standard linear regression model and t…