research
∙
03/10/2022
Bias-variance decomposition of overparameterized regression with random linear features
In classical statistics, the bias-variance trade-off describes how varyi...
share
research
∙
03/25/2021
The Geometry of Over-parameterized Regression and Adversarial Perturbations
Classical regression has a simple geometric description in terms of a pr...
research
∙
12/01/2020
The Perturbative Resolvent Method: spectral densities of random matrix ensembles via perturbation theory
We present a simple, perturbative approach for calculating spectral dens...
research
∙
10/26/2020
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models
The bias-variance trade-off is a central concept in supervised learning....
research
∙
08/02/2019
Machine Learning as Ecology
Machine learning methods have had spectacular success on numerous proble...
research
∙
03/23/2018
A high-bias, low-variance introduction to Machine Learning for physicists
Machine Learning (ML) is one of the most exciting and dynamic areas of m...
research
∙
09/12/2016
Comment on "Why does deep and cheap learning work so well?" [arXiv:1608.08225]
In a recent paper, "Why does deep and cheap learning work so well?", Lin...
research
∙
11/03/2014
Bayesian feature selection with strongly-regularizing priors maps to the Ising Model
Identifying small subsets of features that are relevant for prediction a...
research
∙
10/14/2014
An exact mapping between the Variational Renormalization Group and Deep Learning
Deep learning is a broad set of techniques that uses multiple layers of ...
research
∙
07/30/2014
