# Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)

We introduce a kernel approximation strategy that enables computation of the Gaussian process log marginal likelihood and all hyperparameter derivatives in $\mathcal{O}(p)$ time.

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# Comparing Dynamics: Deep Neural Networks versus Glassy Systems

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems.

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# LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration

We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution.

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# On the Power of Over-parametrization in Neural Networks with Quadratic Activation

We provide new theoretical insights on why over-parametrization is effective in learning neural networks.

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