19 citations · 94 across the 16 of their papers we have counts for
6 papers · 1 filter
Algorithms for Estimating Trends in Global Temperature Volatility
Arash Khodadadi, Daniel J McDonald
Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating…
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets
Darren Homrighausen, Daniel J. McDonald
In this paper we analyze approximate methods for undertaking a principal components analysis (PCA) on large data sets. PCA is a classical dimension reduction method that involves t…
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…