AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is proved that such speedups in sample-complexity require only $\tilde O\left( 1 \right)$ communication, substantially improving upon the communication costs in prior work.
Stochastic gradient descent (SGD) is the primary workhorse for large-scale optimization. While the average behavior of its iterates, typically characterized by mean-squared error bounds, is well-understood, obtaining high-probability guarantees for the last iterate remains challenging. Prior approaches to this problem...
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