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Tolerant algorithms for learning with arbitrary covariate shift
We study the problem of learning under arbitrary distribution shift, where the learner is
trained on a labeled set from one distribution but evaluated on a different, potentially …
trained on a labeled set from one distribution but evaluated on a different, potentially …
Efficient discrepancy testing for learning with distribution shift
A fundamental notion of distance between train and test distributions from the field of domain
adaptation is discrepancy distance. While in general hard to compute, here we provide the …
adaptation is discrepancy distance. While in general hard to compute, here we provide the …
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
We give the first provably efficient algorithms for learning neural networks with distribution
shift. We work in the Testable Learning with Distribution Shift framework (TDS learning) of …
shift. We work in the Testable Learning with Distribution Shift framework (TDS learning) of …
A duality framework for analyzing random feature and two-layer neural networks
We consider the problem of learning functions within the $\mathcal {F} _ {p,\pi} $ and Barron
spaces, which play crucial roles in understanding random feature models (RFMs), two-layer …
spaces, which play crucial roles in understanding random feature models (RFMs), two-layer …