
What causes the test error? Going beyond biasvariance via ANOVA
Modern machine learning methods are often overparametrized, allowing ada...
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DeltaGrad: Rapid retraining of machine learning models
Machine learning models are not static and may need to be retrained on s...
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How to reduce dimension with PCA and random projections?
In our "big data" age, the size and complexity of data is steadily incre...
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The Implicit Regularization of Stochastic Gradient Flow for Least Squares
We study the implicit regularization of minibatch stochastic gradient d...
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Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods
We provide an exact analysis of the limiting spectrum of matrices random...
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Implicit Regularization of Normalization Methods
Normalization methods such as batch normalization are commonly used in o...
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Ridge Regression: Structure, CrossValidation, and Sketching
We study the following three fundamental problems about ridge regression...
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Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond
Many complex deep learning models have found success by exploiting symme...
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Oneshot distributed ridge regression in high dimensions
In many areas, practitioners need to analyze large datasets that challen...
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A New Theory for Sketching in Linear Regression
Large datasets create opportunities as well as analytic challenges. A re...
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Distributed linear regression by averaging
Modern massive datasets pose an enormous computational burden to practit...
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Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Drawing statistical inferences from large datasets in a modelrobust way...
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Flexible Multiple Testing with the FACT Algorithm
Modern highthroughput science often leads to multiple testing problems:...
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Deterministic parallel analysis
Factor analysis is widely used in many application areas. The first step...
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Edgar Dobriban
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