32 citations · 56 across the 6 of their papers we have counts for
6 papers
Bootstrap Error Estimation and Sketch-Size Selection for Sketched Ridge Regression
Akito Narahara, Takayuki Kawashima, Takafumi Kanamori
Randomized sketching reduces the computational cost of large ridge-regression problems, but the coefficient error depends on the realized sketch. We extend the paired-row bootstrap…
Robust Estimation for Kernel Exponential Families with Smoothed Total Variation Distances
Takafumi Kanamori, Kodai Yokoyama, Takayuki Kawashima
In statistical inference, we commonly assume that samples are independent and identically distributed from a probability distribution included in a pre-specified statistical model.…
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 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,…
Robust and Sparse Regression via -divergence
Takayuki Kawashima, Hironori Fujisawa
In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been st…