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Regret minimization in stochastic non-convex learning via a proximal-gradient approach
In this setting, the minimization of external regret is beyond reach for first-order methods, so we focus on a local regret measure defined via a proximal-gradient mapping.
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
We demonstrate two new important properties of the 1-path-norm of shallow neural networks.
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems.
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