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Huber loss
A Robust Hybrid of Lasso and Ridge Regression
Lecture 22 - 11/19/2019 Lecture 22: Robust Location Estimation Lecturer: Jiantao Jiao Scribe: Vignesh Subramanian
Robust Regression Through the Huber's Criterion and Adaptive Lasso
Sketching for M-Estimators and Robust Numerical Linear Algebra
A General and Adaptive Robust Loss Function
ERM and RERM Are Optimal Estimators for Regression Problems When Malicious Outliers Corrupt the Labels
Ensemble Methods - Boosting
Benchmarking Daily Line Loss Rates of Low Voltage Transformer Regions in Power Grid Based on Robust Neural Network
Consistent Regression When Oblivious Outliers Overwhelm
Statistica Sinica Preprint No: SS-2019-0324
Robust Regression Implementation Scottish Hill Races
Generalized Huber Loss for Robust Learning and Its Efficient
Robust Regression Through the Huber's Criterion and Adaptive Lasso
Active Regression with Adaptive Huber Loss Jacopo Cavazza and Vittorio Murino, Senior Member, IEEE
Boosting Algorithms: Regularization, Prediction and Model Fitting
A Robust Boosting Algorithm for Chemical Modeling
Outlier-Robust Estimation of a Sparse Linear Model Using L1-Penalized
A New Principle for Tuning-Free Huber Regression
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Supplementary
A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
A General and Adaptive Robust Loss Function
Forest-Type Regression with General Losses and Robust Forest
Adaptive Huber Regression∗ Arxiv:1706.06991V2 [Math.ST]
Lecture 14 — October 13 14.1 Robust Statistics
The Influence Function of Penalized Regression Estimators Öllerer V, Croux C, Alfons A
Robust Estimation and Applications in Robotics
A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments Using LIDAR Measurements
M-Estimation in Low-Rank Matrix Factorization: a General Framework
Adaptive Huber Regression