19 citations · 57 across the 9 of their papers we have counts for
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stat.ML2011★ 6 cited
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…
stat.ML2011★ 7 cited
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…