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03/09/2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Neural kernels have drastically increased performance on diverse and non...
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06/21/2022
Ensembling over Classifiers: a Bias-Variance Perspective
Ensembles are a straightforward, remarkably effective method for improvi...
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06/15/2022
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Stochastic gradient descent (SGD) is a pillar of modern machine learning...
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05/14/2022
Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties
We develop a stochastic differential equation, called homogenized SGD, f...
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02/08/2022
Understanding the bias-variance tradeoff of Bregman divergences
This paper builds upon the work of Pfau (2013), which generalized the bi...
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11/16/2021
Covariate Shift in High-Dimensional Random Feature Regression
A significant obstacle in the development of robust machine learning mod...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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11/04/2020
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition
Classical learning theory suggests that the optimal generalization perfo...
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10/14/2020
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Modern deep learning models have achieved great success in predictive ac...
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08/15/2020
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Modern deep learning models employ considerably more parameters than req...
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07/31/2020
Cold Posteriors and Aleatoric Uncertainty
Recent work has observed that one can outperform exact inference in Baye...
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07/31/2020
Finite Versus Infinite Neural Networks: an Empirical Study
We perform a careful, thorough, and large scale empirical study of the c...
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12/02/2019
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning
One of the distinguishing characteristics of modern deep learning system...
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10/30/2019
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
We investigate under and overfitting in Generative Adversarial Networks ...
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10/20/2019
Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing...
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04/30/2019