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>> Yeah. So, I'll be talking about momentum methods and acceleration and stochastic optimization. This is joint work with Prateek Jain back in Microsoft Research in India, Sham Kakade who's going to be at the University of Washington and Aaron Sidford from the Stanford University. Okay. Let me start by giving a brief overview and context of our work. As many of us might already be aware of, optimization is a really crucial component in large-scale machine learning these days. Stochastic methods, such as Stochastic Gradient Descent, are some of the workhorses that power this optimization routines. There are three key aspects with which Stochastic Gradient Descent is used in practice in this optimization routines and which are crucial for its good performance. So, the first one is minibatching where we compute the Stochastic Gradient not over a single example but rather over a batch of examples. The second one is what is known as model averaging, where you r

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Convergence Rates of Spectral Distributions of Large Sample Form

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