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This paper presents a multiscale reward hedging method for learning from correct demonstrations, extending guarantees to continuous reward classes with a horizon-free bound via metric entropy, and shows polynomial-time cases for specific settings.
This paper proves a conjectured law of robustness for two-layer neural networks with unbounded weights, showing that a network fitting noisy data must have a Lipschitz constant at least of order sqrt(n/m), up to a logarithmic factor, for continuous piecewise-linear activations like ReLU.