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Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes
We present a distributionally robust formulation of a stochastic optimization problem for non-i. i. d vector autoregressive data.
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
The result formalizes the representation and approximation benefits of neural networks.
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