35 citations · 176 across the 22 of their papers we have counts for
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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…
On Difference Between Two Types of -divergence for Regression
Takayuki Kawashima, Hironori Fujisawa
The -divergence is well-known for having strong robustness against heavy contamination. By virtue of this property, many applications via the -divergence have been proposed.…
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…
Robust and Sparse Regression in GLM by Stochastic Optimization
Takayuki Kawashima, Hironori Fujisawa
The generalized linear model (GLM) plays a key role in regression analyses. In high-dimensional data, the sparse GLM has been used but it is not robust against outliers. Recently,…