Algorithms and matching lower bounds for approximately-convex optimization

NeurIPS 2016 Andrej RisteskiYuanzhi Li

In recent years, a rapidly increasing number of applications in practice requires solving non-convex objectives, like training neural networks, learning graphical models, maximum likelihood estimation etc. Though simple heuristics such as gradient descent with very few modifications tend to work well, theoretical understanding is very weak... (read more)

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