22 citations · 71 across the 18 of their papers we have counts for
18 papers
RandALO: Out-of-sample risk estimation in no time flat
Parth Nobel, Daniel LeJeune, Emmanuel J. Candès
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to op…
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Michał Dereziński, Daniel LeJeune, Deanna Needell +1
Despite being a key bottleneck in many machine learning tasks, the cost of solving large linear systems has proven challenging to quantify due to problem-dependent quantities such…
Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning
Pratik Patil, Daniel LeJeune
We employ random matrix theory to establish consistency of generalized cross validation (GCV) for estimating prediction risks of sketched ridge regression ensembles, enabling effic…
An Adaptive Tangent Feature Perspective of Neural Networks
Daniel LeJeune, Sina Alemohammad
In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to…
Self-Consuming Generative Models Go MAD
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi +5
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this…
WIRE: Wavelet Implicit Neural Representations
Vishwanath Saragadam, Daniel LeJeune, Jasper Tan +3
Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function emp…