We consider the regime where n d.

In all of these studies, classic algorithms are parallelized by simply running parallel and asynchronous model updates without locks. These lock-free, asynchronous algorithms exhibit speedups even when applied to large, non-convex problems, as demonstrated by deep learning systems such as Google's Downpour SGD [6] and Microsoft's Project Adam [4]. While these techniques have been remarkably successful, many of the above papers require delicate and tailored analyses to quantify the benefits of asynchrony for each particular learning task. Moreover, in non-convex settings, we currently have little quantitative insight into how much speedup is gained from asynchrony.

Neural Information Processing Systems http://nips.cc/

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n