4 citations · 5 across the 6 of their papers we have counts for
6 papers
A Geometric Analysis of PCA
Ayoub El Hanchi, Murat Erdogdu, Chris Maddison
What property of the data distribution determines the excess risk of principal component analysis? In this paper, we provide a precise answer to this question. We establish a centr…
On the Efficiency of ERM in Feature Learning
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
Given a collection of feature maps indexed by a set , we study the performance of empirical risk minimization (ERM) on regression problems with square loss over the un…
Minimax Linear Regression under the Quantile Risk
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
We study the problem of designing minimax procedures in linear regression under the quantile risk. We start by considering the realizable setting with independent Gaussian noise, w…
Optimal Excess Risk Bounds for Empirical Risk Minimization on -Norm Linear Regression
Ayoub El Hanchi, Murat A. Erdogdu
We study the performance of empirical risk minimization on the -norm linear regression problem for . We show that, in the realizable case, under no moment ass…
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison
Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contra…
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes
Ayoub El Hanchi, David A. Stephens
Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms. One way of achieving var…