Learning from weakly dependent data under Dobrushin's condition

no code implementations • 21 Jun 2019 • Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti

Indeed, we show that the standard complexity measures of Gaussian and Rademacher complexities and VC dimension are sufficient measures of complexity for the purposes of bounding the generalization error and learning rates of hypothesis classes in our setting.

Generalization Bounds Learning Theory +2

HOGWILD!-Gibbs can be PanAccurate

no code implementations • NeurIPS 2018 • Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti

Hence, the expectation of any function that is Lipschitz with respect to a power of the Hamming distance, can be estimated with a bias that grows logarithmically in $n$.