Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
| Autor: | Kavis, Ali, Skoulakis, Stratis, Antonakopoulos, Kimon, Dadi, Leello Tadesse, Cevher, Volkan |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $L$-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan & Streeter, 2010], but a fairly distinct, adaptive step-size schedule with the recursive stochastic path integrated estimator proposed in [Fang et al., 2018]. To our knowledge, Adaspider is the first parameter-free non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant $L$, target accuracy $\epsilon$ or any bound on gradient norms. In doing so, we are able to compute an $\epsilon$-stationary point with $\tilde{O}\left(n + \sqrt{n}/\epsilon^2\right)$ oracle-calls, which matches the respective lower bound up to logarithmic factors. Comment: 23 pages, 2 figures, accepted at NeurIPS 2022 |
| Databáze: | arXiv |
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