Robust estimation with Lasso when outputs are adversarially contaminated
Fuente:
arXiv
Saved in:
| Main Authors: | Sasai, Takeyuki, Fujisawa, Hironori |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
by: Sasai, Takeyuki, et al.
Published: (2022)
by: Sasai, Takeyuki, et al.
Published: (2022)
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
by: Sasai, Takeyuki, et al.
Published: (2020)
by: Sasai, Takeyuki, et al.
Published: (2020)
Adversarial robust weighted Huber regression
by: Sasai, Takeyuki, et al.
Published: (2021)
by: Sasai, Takeyuki, et al.
Published: (2021)
An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
by: Sasai, Takeyuki, et al.
Published: (2025)
by: Sasai, Takeyuki, et al.
Published: (2025)
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023)
by: Takada, Masaaki, et al.
Published: (2023)
Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
by: Sasai, Takeyuki, et al.
Published: (2024)
by: Sasai, Takeyuki, et al.
Published: (2024)
Estimation of sparse linear regression coefficients under $L$-subexponential covariates
by: Sasai, Takeyuki
Published: (2023)
by: Sasai, Takeyuki
Published: (2023)
Learning Survival Models with Right-Censored Reporting Delays
by: Shikuri, Yuta, et al.
Published: (2025)
by: Shikuri, Yuta, et al.
Published: (2025)
Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)
by: Puchkin, Nikita, et al.
Published: (2021)
Spacing Test for Fused Lasso
by: Tasaka, Rieko, et al.
Published: (2025)
by: Tasaka, Rieko, et al.
Published: (2025)
On the design-dependent suboptimality of the Lasso
by: Pathak, Reese, et al.
Published: (2024)
by: Pathak, Reese, et al.
Published: (2024)
On Consistency of Signature Using Lasso
by: Guo, Xin, et al.
Published: (2023)
by: Guo, Xin, et al.
Published: (2023)
$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
by: Fujisawa, Masahiro, et al.
Published: (2025)
by: Fujisawa, Masahiro, et al.
Published: (2025)
Information-theoretic Generalization Analysis for Expected Calibration Error
by: Futami, Futoshi, et al.
Published: (2024)
by: Futami, Futoshi, et al.
Published: (2024)
On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)
by: Peng, Jingfu, et al.
Published: (2026)
Winsorized mean estimation with heavy tails and adversarial contamination
by: Kock, Anders Bredahl, et al.
Published: (2025)
by: Kock, Anders Bredahl, et al.
Published: (2025)
Top-$K$ ranking with a monotone adversary
by: Yang, Yuepeng, et al.
Published: (2024)
by: Yang, Yuepeng, et al.
Published: (2024)
Minimax rates of convergence for nonparametric regression under adversarial attacks
by: Peng, Jingfu, et al.
Published: (2024)
by: Peng, Jingfu, et al.
Published: (2024)
Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of Automatic Relevance Determination
by: Yoshida, Tsukasa, et al.
Published: (2025)
by: Yoshida, Tsukasa, et al.
Published: (2025)
Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators
by: Wang, Longlin, et al.
Published: (2025)
by: Wang, Longlin, et al.
Published: (2025)
On Difference Between Two Types of $γ$-divergence for Regression
by: Kawashima, Takayuki, et al.
Published: (2018)
by: Kawashima, Takayuki, et al.
Published: (2018)
Lasso and Partially-Rotated Designs
by: Buhai, Rares-Darius
Published: (2025)
by: Buhai, Rares-Darius
Published: (2025)
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
by: Hassani, Hamed, et al.
Published: (2022)
by: Hassani, Hamed, et al.
Published: (2022)
Lasso Penalization for High-Dimensional Beta Regression Models: Computation, Analysis, and Inference
by: Ramezani, Niloofar, et al.
Published: (2025)
by: Ramezani, Niloofar, et al.
Published: (2025)
Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
by: Liu, Jingbo
Published: (2024)
by: Liu, Jingbo
Published: (2024)
Robust density estimation over star-shaped density classes
by: Liu, Xiaolong, et al.
Published: (2025)
by: Liu, Xiaolong, et al.
Published: (2025)
Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
by: Lin, Meixia, et al.
Published: (2025)
by: Lin, Meixia, et al.
Published: (2025)
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
by: Axelrod, Brian, et al.
Published: (2019)
by: Axelrod, Brian, et al.
Published: (2019)
Statistical Inference for Linear Functionals of Online Least-squares SGD when $t \gtrsim d^{1+δ}$
by: Agrawalla, Bhavya, et al.
Published: (2025)
by: Agrawalla, Bhavya, et al.
Published: (2025)
On consistent estimation of dimension values
by: Cholaquidis, Alejandro, et al.
Published: (2024)
by: Cholaquidis, Alejandro, et al.
Published: (2024)
Variance estimation in graphs with the fused lasso
by: Padilla, Oscar Hernan Madrid
Published: (2022)
by: Padilla, Oscar Hernan Madrid
Published: (2022)
Sample complexity of Schrödinger potential estimation
by: Puchkin, Nikita, et al.
Published: (2025)
by: Puchkin, Nikita, et al.
Published: (2025)
Precise Asymptotics of Bagging Regularized M-estimators
by: Koriyama, Takuya, et al.
Published: (2024)
by: Koriyama, Takuya, et al.
Published: (2024)
On the number of modes of Gaussian kernel density estimators
by: Geshkovski, Borjan, et al.
Published: (2024)
by: Geshkovski, Borjan, et al.
Published: (2024)
Proper scoring rules for estimation and forecast evaluation
by: Waghmare, Kartik, et al.
Published: (2025)
by: Waghmare, Kartik, et al.
Published: (2025)
Distribution free M-estimation
by: Areces, Felipe, et al.
Published: (2025)
by: Areces, Felipe, et al.
Published: (2025)
Support estimation in high-dimensional heteroscedastic mean regression
by: Hermann, Philipp, et al.
Published: (2020)
by: Hermann, Philipp, et al.
Published: (2020)
Performance of the empirical median for location estimation in heteroscedastic settings
by: Louati, Sirine
Published: (2025)
by: Louati, Sirine
Published: (2025)
Optimal score estimation via empirical Bayes smoothing
by: Wibisono, Andre, et al.
Published: (2024)
by: Wibisono, Andre, et al.
Published: (2024)
A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)
by: Tyralis, Hristos, et al.
Published: (2022)
Similar Items
-
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
by: Sasai, Takeyuki, et al.
Published: (2022) -
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
by: Sasai, Takeyuki, et al.
Published: (2020) -
Adversarial robust weighted Huber regression
by: Sasai, Takeyuki, et al.
Published: (2021) -
An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
by: Sasai, Takeyuki, et al.
Published: (2025) -
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
by: Takada, Masaaki, et al.
Published: (2023)