166 citations · 595 across the 71 of their papers we have counts for
7 papers · 1 filter
Selecting the number of components in PCA via random signflips
David Hong, Yue Sheng, Edgar Dobriban
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selec…
Sparse sketches with small inversion bias
Michał Dereziński, Zhenyu Liao, Edgar Dobriban +1
For a tall matrix and a random sketching matrix , the sketched estimate of the inverse covariance matrix is typically biased: $E[(\…
What causes the test error? Going beyond bias-variance via ANOVA
Licong Lin, Edgar Dobriban
Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level. This can seem puzzling; in the worst case, such models do not need to g…
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
Machine learning models are not static and may need to be retrained on slightly changed datasets, for instance, with the addition or deletion of a set of data points. This has many…
Provable tradeoffs in adversarially robust classification
Edgar Dobriban, Hamed Hassani, David Hong +1
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open…
How to reduce dimension with PCA and random projections?
Fan Yang, Sifan Liu, Edgar Dobriban +1
In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension red…
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression. We leverage a continuous-time s…