19 citations · 94 across the 16 of their papers we have counts for
4 papers · 2 filters
Estimated VC dimension for risk bounds
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish
Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the generalization capacity of learning algorithms. However, apart from a few special cases, it is hard or impossible…
Spectral approximations in machine learning
Darren Homrighausen, Daniel J. McDonald
In many areas of machine learning, it becomes necessary to find the eigenvector decompositions of large matrices. We discuss two methods for reducing the computational burden of sp…
Rademacher complexity of stationary sequences
Daniel J. McDonald, Cosma Rohilla Shalizi
We show how to control the generalization error of time series models wherein past values of the outcome are used to predict future values. The results are based on a generalizatio…
Generalization error bounds for stationary autoregressive models
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish
We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without…