1.9k citations · 6.5k across the 132 of their papers we have counts for
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Uniform Convergence with Square-Root Lipschitz Loss
Lijia Zhou, Zhen Dai, Frederic Koehler +1
We establish generic uniform convergence guarantees for Gaussian data in terms of the Rademacher complexity of the hypothesis class and the Lipschitz constant of the square root of…
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression
Lijia Zhou, James B. Simon, Gal Vardi +1
We study the cost of overfitting in noisy kernel ridge regression (KRR), which we define as the ratio between the test error of the interpolating ridgeless model and the test error…
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
Lijia Zhou, Frederic Koehler, Pragya Sur +2
We prove a new generalization bound that shows for any class of linear predictors in Gaussian space, the Rademacher complexity of the class and the training error under any continu…
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
Lijia Zhou, Frederic Koehler, Danica J. Sutherland +1
We study a localized notion of uniform convergence known as an "optimistic rate" (Panchenko 2002; Srebro et al. 2010) for linear regression with Gaussian data. Our refined analysis…
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting
Frederic Koehler, Lijia Zhou, Danica J. Sutherland +1
We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interp…
Does Invariant Risk Minimization Capture Invariance?
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland +1
We show that the Invariant Risk Minimization (IRM) formulation of Arjovsky et al. (2019) can fail to capture "natural" invariances, at least when used in its practical "linear" for…