Stochastic Gradient Descent with Strategic Querying
This work addresses optimization efficiency for machine learning practitioners, but it is incremental as it builds on existing stochastic gradient methods with a novel querying strategy.
The paper tackles the problem of finite-sum optimization under first-order queries by investigating strategic querying to improve stochastic gradient-based methods, showing that a practical algorithm (SGQ) enhances transient-state performance over SGD while making only one query per iteration.
This paper considers a finite-sum optimization problem under first-order queries and investigates the benefits of strategic querying on stochastic gradient-based methods compared to uniform querying strategy. We first introduce Oracle Gradient Querying (OGQ), an idealized algorithm that selects one user's gradient yielding the largest possible expected improvement (EI) at each step. However, OGQ assumes oracle access to the gradients of all users to make such a selection, which is impractical in real-world scenarios. To address this limitation, we propose Strategic Gradient Querying (SGQ), a practical algorithm that has better transient-state performance than SGD while making only one query per iteration. For smooth objective functions satisfying the Polyak-Lojasiewicz condition, we show that under the assumption of EI heterogeneity, OGQ enhances transient-state performance and reduces steady-state variance, while SGQ improves transient-state performance over SGD. Our numerical experiments validate our theoretical findings.