
OutlierResistant Estimators for Average Treatment Effect in Causal Inference
Estimators for causal quantities sometimes suffer from outliers. We inve...
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Adversarial robust weighted Huber regression
We propose a novel method to estimate the coefficients of linear regress...
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Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
We consider robust low rank matrix estimation when random noise is heavy...
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Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
We consider the problem of inferring the causal structure from observati...
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Transfer Learning via ℓ_1 Regularization
Machine learning algorithms typically require abundant data under a stat...
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Robust estimation with Lasso when outputs are adversarially contaminated
We consider robust estimation when outputs are adversarially contaminate...
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HMLasso: Lasso for High Dimensional and Highly Missing Data
Sparse regression such as Lasso has achieved great success in dealing wi...
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Stochastic Gradient Descent for Stochastic DoublyNonconvex Composite Optimization
The stochastic gradient descent has been widely used for solving composi...
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On Difference Between Two Types of γdivergence for Regression
The γdivergence is wellknown for having strong robustness against heav...
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Robust and sparse Gaussian graphical modeling under cellwise contamination
Graphical modeling explores dependences among a collection of variables ...
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Robust and Sparse Regression in GLM by Stochastic Optimization
The generalized linear model (GLM) plays a key role in regression analys...
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Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Sparse regularization such as ℓ_1 regularization is a quite powerful and...
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Sparse principal component regression for generalized linear models
Principal component regression (PCR) is a widely used twostage procedur...
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Robust and Sparse Regression via γdivergence
In highdimensional data, many sparse regression methods have been propo...
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Sparse principal component regression with adaptive loading
Principal component regression (PCR) is a twostage procedure that selec...
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Affine Invariant Divergences associated with Composite Scores and its Applications
In statistical analysis, measuring a score of predictive performance is ...
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Hironori Fujisawa
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