77 citations · 109 across the 6 of their papers we have counts for
6 papers
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
Xianli Zeng, Kevin Jiang, Guang Cheng +1
Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classifica…
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform
Jonathan Lacotte, Sifan Liu, Edgar Dobriban +1
Random projections or sketching are widely used in many algorithmic and learning contexts. Here we study the performance of iterative Hessian sketch for least-squares problems. By…
A Group-Theoretic Framework for Data Augmentation
Shuxiao Chen, Edgar Dobriban, Jane H Lee
Data augmentation is a widely used trick when training deep neural networks: in addition to the original data, properly transformed data are also added to the training set. However…
Asymptotics for Sketching in Least Squares Regression
Edgar Dobriban, Sifan Liu
We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider…
Optimal prediction in the linearly transformed spiked model
Edgar Dobriban, William Leeb, Amit Singer
We consider the linearly transformed spiked model, where observations are noisy linear transforms of unobserved signals of interest : \begin{align*} Y_i = A_i X_i + \var…
PCA from noisy, linearly reduced data: the diagonal case
Edgar Dobriban, William Leeb, Amit Singer
Suppose we observe data of the form or , , where $D_i \in \mathbb{R…