AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
4 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A momentum-based estimator with mini-batches is employed to track the function values of each level, which are subsequently used to construct momentum gradient estimators, and a batch-free method that incorporates a first-order approximation and a clipping technique for function value estimation is developed.
This paper solves the problem of sign-based variance reduction methods' failure to obtain optimal convergence rates for both nonconvex stochastic and finite-sum optimization, and proposes tracking the global gradient at the server through unbiased compression of recursive gradient increments.
This paper studies projection-free algorithms for stochastic constrained multi-level compositional optimization and proposes variance-reduced projection-free algorithms and establishes complexity guarantees under both the Frank-Wolfe gap and the gradient mapping criteria.
Traditional variance reduction methods (e.g., SPIDER, SARAH, STORM) have been extensively investigated for improving the convergence rates of stochastic optimization. These techniques typically maintain a sequence of estimators for a single function (or gradient) across iterations. However, what if we need to track mul...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.