
Solon: Communicationefficient Byzantineresilient Distributed Training via Redundant Gradients
There has been a growing need to provide Byzantineresilience in distrib...
read it

Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
Modern methods for learning from data depend on many tuning parameters, ...
read it

PAC Prediction Sets Under Covariate Shift
An important challenge facing modern machine learning is how to rigorous...
read it

Consistency of invariancebased randomization tests
Invariancebased randomization tests – such as permutation tests – are a...
read it

Understanding Generalization in Adversarial Training via the BiasVariance Decomposition
Adversarially trained models exhibit a large generalization gap: they ca...
read it

Selecting the number of components in PCA via random signflips
Dimensionality reduction via PCA and factor analysis is an important too...
read it

Sparse sketches with small inversion bias
For a tall n× d matrix A and a random m× n sketching matrix S, the sketc...
read it

What causes the test error? Going beyond biasvariance via ANOVA
Modern machine learning methods are often overparametrized, allowing ada...
read it

DeltaGrad: Rapid retraining of machine learning models
Machine learning models are not static and may need to be retrained on s...
read it

How to reduce dimension with PCA and random projections?
In our "big data" age, the size and complexity of data is steadily incre...
read it

The Implicit Regularization of Stochastic Gradient Flow for Least Squares
We study the implicit regularization of minibatch stochastic gradient d...
read it

Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods
We provide an exact analysis of the limiting spectrum of matrices random...
read it

Implicit Regularization of Normalization Methods
Normalization methods such as batch normalization are commonly used in o...
read it

Ridge Regression: Structure, CrossValidation, and Sketching
We study the following three fundamental problems about ridge regression...
read it

Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond
Many complex deep learning models have found success by exploiting symme...
read it

Oneshot distributed ridge regression in high dimensions
In many areas, practitioners need to analyze large datasets that challen...
read it

A New Theory for Sketching in Linear Regression
Large datasets create opportunities as well as analytic challenges. A re...
read it

Distributed linear regression by averaging
Modern massive datasets pose an enormous computational burden to practit...
read it

Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Drawing statistical inferences from large datasets in a modelrobust way...
read it

Flexible Multiple Testing with the FACT Algorithm
Modern highthroughput science often leads to multiple testing problems:...
read it

Deterministic parallel analysis
Factor analysis is widely used in many application areas. The first step...
read it
Edgar Dobriban
is this you? claim profile