35 citations · 176 across the 22 of their papers we have counts for
22 papers
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,…
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
Takeyuki Sasai, Hironori Fujisawa
We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampl…
Adversarial robust weighted Huber regression
Takeyuki Sasai, Hironori Fujisawa
We consider a robust estimation of linear regression coefficients. In this note, we focus on the case where the covariates are sampled from an -subGaussian distribution with unk…
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
Takeyuki Sasai, Hironori Fujisawa
We consider robust low rank matrix estimation as a trace regression when outputs are contaminated by adversaries. The adversaries are allowed to add arbitrary values to arbitrary o…
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
Kazuharu Harada, Hironori Fujisawa
We consider the problem of inferring the causal structure from observational data, especially when the structure is sparse. This type of problem is usually formulated as an inferen…
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…
Robust estimation with Lasso when outputs are adversarially contaminated
Takeyuki Sasai, Hironori Fujisawa
We consider robust estimation when outputs are adversarially contaminated. Nguyen and Tran (2012) proposed an extended Lasso for robust parameter estimation and then they showed th…
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,…
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
Sparse regularization such as regularization is a quite powerful and widely used strategy for high dimensional learning problems. The effectiveness of sparse regularizatio…
Sparse principal component regression for generalized linear models
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are…
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…
Robust sparse Gaussian graphical modeling
Kei Hirose, Hironori Fujisawa, Jun Sese
Gaussian graphical modeling has been widely used to explore various network structures, such as gene regulatory networks and social networks. We often use a penalized maximum likel…
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…
On the Weak Convergence and Central Limit Theorem of Blurring and Nonblurring Processes with Application to Robust Location Estimation
Ting-Li Chen, Hironori Fujisawa, Su-Yun Huang +1
This article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. Then, they are applied to the estimation of location paramete…
Sparse principal component regression with adaptive loading
Shuichi Kawano, Hironori Fujisawa, Toyoyuki Takada +1
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variab…
Robust Estimation under Heavy Contamination using Enlarged Models
Takafumi Kanamori, Hironori Fujisawa
In data analysis, contamination caused by outliers is inevitable, and robust statistical methods are strongly demanded. In this paper, our concern is to develop a new approach for…
Affine Invariant Divergences associated with Composite Scores and its Applications
Takafumi Kanamori, Hironori Fujisawa
In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at han…
Approximate tail probabilities of the maximum of a chi-square field on multi-dimensional lattice points and their applications to detection of loci interactions
Satoshi Kuriki, Yoshiaki Harushima, Hironori Fujisawa +1
Define a chi-square random field on a multi-dimensional lattice points index set with a direct-product covariance structure, and consider the distribution of the maximum of this ra…