23 citations · 30 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
Better Supervisory Signals by Observing Learning Paths
Yi Ren, Shangmin Guo, Danica J. Sutherland
Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing…
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…
On Uniform Convergence and Low-Norm Interpolation Learning
Lijia Zhou, Danica J. Sutherland, Nathan Srebro
We consider an underdetermined noisy linear regression model where the minimum-norm interpolating predictor is known to be consistent, and ask: can uniform convergence in a norm ba…
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu, Wenkai Xu, Jie Lu +3
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels…
Learning deep kernels for exponential family densities
Li Wenliang, Danica J. Sutherland, Heiko Strathmann +1
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a…