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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.
Agnostic federated learning (AFL) seeks a model that performs reliably across $m$ heterogeneous workers, but communication remains a bottleneck. We improve communication efficiency by reducing the number of synchronization rounds via faster convergence and the communication cost per round via compression. We first prop...
In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-varying comparator sequence. Despite considerable advances, attaining optimal bounds for strongly convex and exp-concave losses often involves intricate analysis. In th...
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...
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