mlsanityData to 7 Oct 2026

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions

arXiv 2406.01959 ↗First posted 4 Jun 2024math.OC cs.LGcited by 5

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Abstract (arXiv)

This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique. Existing adaptive extensions of STORM rely on strong assumptions like bounded gradients and bounded function values, or suffer an additional O(log T) term in the convergence rate. To address these limitations, we introduce a novel adaptive STORM method that achieves an optimal convergence rate of O(T-1/3) for non-convex functions with our newly designed learning rate strategy. Compared with existing approaches, our method requires weaker assumptions and attains the optimal convergence rate without the additional O(log T) term. We also extend the proposed technique to stochastic compositional optimization, obtaining the same optimal rate of O(T-1/3). Furthermore, we investigate the non-convex finite-sum problem and develop another innovative adaptive variance reduction method that achieves an optimal convergence rate of O(n1/4 T-1/2 ), where n represents the number of component functions. Numerical experiments across various tasks validate the effectiveness of our method.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 4 Jun 2024.

Builds on

31 of 45 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.