ReLU networks approximate traceable definable subsets of the unit cube in L^p with size O(ε^{-p(n-1)/m}) and yield ERM learning rates of order N^{-m/(m+pn-p)} for hinge loss under uniform component bounds.
Neural Networks , volume=
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DQPOPE estimates the entire return distribution in off-policy evaluation via deep quantile process regression, providing statistical advantages over standard single-value methods with equivalent sample sizes.
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Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks
ReLU networks approximate traceable definable subsets of the unit cube in L^p with size O(ε^{-p(n-1)/m}) and yield ERM learning rates of order N^{-m/(m+pn-p)} for hinge loss under uniform component bounds.
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Distributional Off-Policy Evaluation with Deep Quantile Process Regression
DQPOPE estimates the entire return distribution in off-policy evaluation via deep quantile process regression, providing statistical advantages over standard single-value methods with equivalent sample sizes.