scientific article; zbMATH DE number 7625163
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Publication:5054595
Florentina Bunea, Seth Strimas-Mackey, Marten H. Wegkamp
Publication date: 29 November 2022
Full work available at URL: https://arxiv.org/abs/2002.02525
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
predictioninterpolationfactor modelshigh-dimensional regressionfinite sample risk boundsminimum-norm predictor
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Cites Work
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- Sufficient forecasting using factor models
- High-dimensional covariance matrix estimation in approximate factor models
- Surprises in high-dimensional ridgeless least squares interpolation
- Model assisted variable clustering: minimax-optimal recovery and algorithms
- Just interpolate: kernel ``ridgeless regression can generalize
- Some contributions to maximum likelihood factor analysis
- Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression
- Sparse Bayesian infinite factor models
- Modern Multivariate Statistical Techniques
- Smallest singular value of a random rectangular matrix
- Forecasting Using Principal Components From a Large Number of Predictors
- High-Dimensional Probability
- Two Models of Double Descent for Weak Features
- The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve
- Benign overfitting in linear regression
- Benefit of Interpolation in Nearest Neighbor Algorithms
- Does learning require memorization? a short tale about a long tail
- Reconciling modern machine-learning practice and the classical bias–variance trade-off
- High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
- A general method for analysis of covariance structures
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
